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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Ma, W"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13280">arXiv:2411.13280</a> <span> [<a href="https://arxiv.org/pdf/2411.13280">pdf</a>, <a href="https://arxiv.org/format/2411.13280">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Structure-Based Molecule Optimization via Gradient-Guided Bayesian Update </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qiu%2C+K">Keyue Qiu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+J">Jie Yu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hongbo Ma</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+Z">Ziyao Cao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Z">Zhilong Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+Y">Yushuai Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13280v2-abstract-short" style="display: inline;"> Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and diffe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13280v2-abstract-full').style.display = 'inline'; document.getElementById('2411.13280v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13280v2-abstract-full" style="display: none;"> Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the first gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. Our proposed MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3% , Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility and potential. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13280v2-abstract-full').style.display = 'none'; document.getElementById('2411.13280v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 17 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.10516">arXiv:2410.10516</a> <span> [<a href="https://arxiv.org/pdf/2410.10516">pdf</a>, <a href="https://arxiv.org/format/2410.10516">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> UniGEM: A Unified Approach to Generation and Property Prediction for Molecules </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+Y">Yan Lu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhi-Ming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.10516v1-abstract-short" style="display: inline;"> Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrate that diffusion model, a prominent generative approach, can learn meaningful data representations that enhance predictive tasks, we explore the potential for developing a unified generative model in the molecular domain… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10516v1-abstract-full').style.display = 'inline'; document.getElementById('2410.10516v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10516v1-abstract-full" style="display: none;"> Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrate that diffusion model, a prominent generative approach, can learn meaningful data representations that enhance predictive tasks, we explore the potential for developing a unified generative model in the molecular domain that effectively addresses both molecular generation and property prediction tasks. However, the integration of these tasks is challenging due to inherent inconsistencies, making simple multi-task learning ineffective. To address this, we propose UniGEM, the first unified model to successfully integrate molecular generation and property prediction, delivering superior performance in both tasks. Our key innovation lies in a novel two-phase generative process, where predictive tasks are activated in the later stages, after the molecular scaffold is formed. We further enhance task balance through innovative training strategies. Rigorous theoretical analysis and comprehensive experiments demonstrate our significant improvements in both tasks. The principles behind UniGEM hold promise for broader applications, including natural language processing and computer vision. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10516v1-abstract-full').style.display = 'none'; document.getElementById('2410.10516v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09543">arXiv:2410.09543</a> <span> [<a href="https://arxiv.org/pdf/2410.09543">pdf</a>, <a href="https://arxiv.org/format/2410.09543">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiao%2C+X">Xiaoran Jiao</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+W">Weian Mao</a>, <a href="/search/q-bio?searchtype=author&query=Jin%2C+W">Wengong Jin</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+P">Peiyuan Yang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Hao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+C">Chunhua Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09543v1-abstract-short" style="display: inline;"> Predicting the change in binding free energy ($螖螖G$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $螖螖G$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained invers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09543v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09543v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09543v1-abstract-full" style="display: none;"> Predicting the change in binding free energy ($螖螖G$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $螖螖G$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to $螖螖G$ prediction. We begin by analyzing the thermodynamic definition of $螖螖G$ and introducing the Boltzmann distribution to connect energy with protein conformational distribution. However, the protein conformational distribution is intractable; therefore, we employ Bayes' theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for $螖螖G$ estimation. Compared to previous inverse folding-based methods, our method explicitly accounts for the unbound state of protein complex in the $螖螖G$ thermodynamic cycle, introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised), significantly surpassing the previously reported SoTA values of 0.2632 and 0.4324, respectively. Futhermore, we demonstrate the capability of our method on binding energy prediction, protein-protein docking and antibody optimization tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09543v1-abstract-full').style.display = 'none'; document.getElementById('2410.09543v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03328">arXiv:2410.03328</a> <span> [<a href="https://arxiv.org/pdf/2410.03328">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Double-Strand Break Clustering: An Economical and Effective Strategy for DNA Repair </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junyi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wenzong Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Y">Yuqi Ma</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+G">Gen Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03328v1-abstract-short" style="display: inline;"> In mammalian cells, repair centers for DNA double-strand breaks (DSBs) have been identified. However, previous researches predominantly rely on methods that induce specific DSBs by cutting particular DNA sequences. The clustering and its spatiotemporal properties of non-specifically DSBs, especially those induced by environmental stresses such as irradiation, remains unclear. In this study, we use… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03328v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03328v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03328v1-abstract-full" style="display: none;"> In mammalian cells, repair centers for DNA double-strand breaks (DSBs) have been identified. However, previous researches predominantly rely on methods that induce specific DSBs by cutting particular DNA sequences. The clustering and its spatiotemporal properties of non-specifically DSBs, especially those induced by environmental stresses such as irradiation, remains unclear. In this study, we used Dragonfly microscopy to induce high-precision damage in cells and discovered that DSB clustering during the early stages of DNA damage response (DDR) and repair, but not during the repair plateau phase. Early in DDR, DSB clustered into existing 53BP1 foci. The DSB clustering at different stages has different implications for DNA repair. By controlling the distance between adjacent damage points, we found that the probability of DSB clustering remains constant at distances of 0.8 - 1.4 um, while clustering does not occur beyond 1.4 um. Within the 0.8 um range, the probability of clustering significantly increases due to the phase separation effect of 53BP1. Using a Monte Carlo approach, we developed a dynamic model of 53BP1 foci formation, fission, and fusion. This model accurately predicts experimental outcomes and further demonstrates the temporal and spatial influences on DSB clustering. These results showed that, similarly to specifically induced DSBs, non-specifically induced DSBs can also cluster. The extent of DSB clustering is influenced by both temporal and spatial factors, which provide new insights into the dynamics of DSB clustering and the role of 53BP1 in DNA repair processes. Such findings could enhance our understanding of DNA damage responses and help us improve DNA repair therapies in disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03328v1-abstract-full').style.display = 'none'; document.getElementById('2410.03328v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.12034">arXiv:2408.12034</a> <span> [<a href="https://arxiv.org/pdf/2408.12034">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Sex chromosome evolution: The classical paradigm and so much beyond </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Veltsos%2C+P">Paris Veltsos</a>, <a href="/search/q-bio?searchtype=author&query=Shinde%2C+S">Sagar Shinde</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wen-Juan Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.12034v1-abstract-short" style="display: inline;"> Sex chromosomes have independently evolved in species with separate sexes in most lineages across the tree of life. However, the well-accepted canonical model of sex chromosome evolution is not universally supported. There is no single trajectory for sex chromosome formation and evolution across the tree of life, suggesting the underlying mechanisms and evolutionary forces are diverse and lineage… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12034v1-abstract-full').style.display = 'inline'; document.getElementById('2408.12034v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.12034v1-abstract-full" style="display: none;"> Sex chromosomes have independently evolved in species with separate sexes in most lineages across the tree of life. However, the well-accepted canonical model of sex chromosome evolution is not universally supported. There is no single trajectory for sex chromosome formation and evolution across the tree of life, suggesting the underlying mechanisms and evolutionary forces are diverse and lineage specific. We review the diversity of sex chromosome systems, describe the canonical model of sex chromosome evolution, and summarize studies challenging various aspects of this model. They include evidence that many lineages experience frequent sex chromosome turnovers or maintain homomorphic sex chromosomes over long periods of time, suggesting sex chromosome degeneration is not inevitable. Sometimes the sex-limited Y/W chromosomes expand before they contract in size. Both transposable elements and gene gains could contribute to this size expansion, which further challenges gene loss being the hallmark of sex chromosome degeneration. Finally, empirical support for the role of sexually antagonistic selection as a driver of recombination suppression on sex chromosomes remains elusive. We summarize models that result in loss of recombination without invoking sexually antagonistic selection, which have not been empirically verified yet, and suggest future avenues for sex chromosome research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12034v1-abstract-full').style.display = 'none'; document.getElementById('2408.12034v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This has been accepted as a Book chapter for Encyclopedia of Evolutionary Biology book 2nd Edition</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.12060">arXiv:2407.12060</a> <span> [<a href="https://arxiv.org/pdf/2407.12060">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Decoding Dmrt1: Insights into vertebrate sex determination and gonadal sex differentiation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Augstenov%C3%A1%2C+B">Barbora Augstenov谩</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wen-Juan Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.12060v2-abstract-short" style="display: inline;"> Dmrt1 is pivotal in testis formation and function by interacting with genes crucial for Sertoli cell differentiation, such as Sox9. It represses female-determining pathways and ovarian formation by silencing Foxl2. Across 127 vertebrate species, Dmrt1 exhibits sexually dimorphic expression, prior to and during gonadal sex differentiation and in adult testes, implicating its role in master regulati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12060v2-abstract-full').style.display = 'inline'; document.getElementById('2407.12060v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12060v2-abstract-full" style="display: none;"> Dmrt1 is pivotal in testis formation and function by interacting with genes crucial for Sertoli cell differentiation, such as Sox9. It represses female-determining pathways and ovarian formation by silencing Foxl2. Across 127 vertebrate species, Dmrt1 exhibits sexually dimorphic expression, prior to and during gonadal sex differentiation and in adult testes, implicating its role in master regulation of sex determination and gonadal sex differentiation. Dmrt1 emerges as a master sex-determining gene in one fish, frog, chicken and reptile. Recent studies suggest epigenetic regulation of Dmrt1 in its promoter methylation and transposable element insertion introducing epigenetic modification to cis-regulatory elements, alongside non-coding RNA involvement, in a wide spectrum of sex-determining mechanisms. Additionally, alternative splicing of Dmrt1 was found in all vertebrate groups except amphibians. Dmrt1 has evolved many lineage-specific isoforms (ranging from 2 to 10) but has no sex-specific splicing variants in any taxa, which is in sharp contrast to the constitutional sex-specific splicing of Dsx in insects. Future research should focus on understanding the molecular basis of environmental sex determination from a broader taxon, and the molecular basis of epigenetic regulation. It is also essential to understand why and how multiple alternative splicing variants of Dmrt1 evolve and the specific roles each isoform plays in sex determination and gonadal sex differentiation, as well as the significant differences in the molecular mechanisms and functions of alternative splicing between Dmrt1 in vertebrates and sex-specific splicing of Dsx in insects. Understanding the differences could provide deeper insights into the evolution of sex-determining mechanisms between vertebrates and insects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12060v2-abstract-full').style.display = 'none'; document.getElementById('2407.12060v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">40 pages, 4 figures, 2 tables, 1 supplementary table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.08980">arXiv:2406.08980</a> <span> [<a href="https://arxiv.org/pdf/2406.08980">pdf</a>, <a href="https://arxiv.org/format/2406.08980">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gao%2C+B">Bowen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Tan%2C+H">Haichuan Tan</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yanwen Huang</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+M">Minsi Ren</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+X">Xiao Huang</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Ya-Qin Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.08980v1-abstract-short" style="display: inline;"> Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08980v1-abstract-full').style.display = 'inline'; document.getElementById('2406.08980v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.08980v1-abstract-full" style="display: none;"> Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability of the Vina docking score, the current standard for assessing binding abilities, is increasingly questioned due to its susceptibility to overfitting. To address these limitations, we propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds, introducing a virtual screening-based metric for practical deployment capabilities, and re-evaluating binding affinity more rigorously. Our experiments reveal that while current SBDD models achieve high Vina scores, they fall short in practical usability metrics, highlighting a significant gap between theoretical predictions and real-world applicability. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models and aligning them more closely with the needs of pharmaceutical research and development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08980v1-abstract-full').style.display = 'none'; document.getElementById('2406.08980v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.08961">arXiv:2406.08961</a> <span> [<a href="https://arxiv.org/pdf/2406.08961">pdf</a>, <a href="https://arxiv.org/format/2406.08961">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yanwen Huang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+B">Bowen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Jia%2C+Y">Yinjun Jia</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hongbo Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Ya-Qin Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.08961v1-abstract-short" style="display: inline;"> Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics. The term "bioactivity" encompasses various biological effects resulting from these interactions, including both binding and functional responses. The magnitude of bioactivity dictates the therapeutic or t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08961v1-abstract-full').style.display = 'inline'; document.getElementById('2406.08961v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.08961v1-abstract-full" style="display: none;"> Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics. The term "bioactivity" encompasses various biological effects resulting from these interactions, including both binding and functional responses. The magnitude of bioactivity dictates the therapeutic or toxic pharmacological outcomes of small molecules, rendering accurate bioactivity prediction crucial for the development of safe and effective drugs. However, existing structural datasets of small molecule-protein interactions are often limited in scale and lack systematically organized bioactivity labels, thereby impeding our understanding of these interactions and precise bioactivity prediction. In this study, we introduce a comprehensive dataset of small molecule-protein interactions, consisting of over a million binding structures, each annotated with real biological activity labels. This dataset is designed to facilitate unbiased bioactivity prediction. We evaluated several classical models on this dataset, and the results demonstrate that the task of unbiased bioactivity prediction is challenging yet essential. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08961v1-abstract-full').style.display = 'none'; document.getElementById('2406.08961v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.03141">arXiv:2406.03141</a> <span> [<a href="https://arxiv.org/pdf/2406.03141">pdf</a>, <a href="https://arxiv.org/format/2406.03141">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Floating Anchor Diffusion Model for Multi-motif Scaffolding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+K">Ke Liu</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+W">Weian Mao</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+S">Shuaike Shen</a>, <a href="/search/q-bio?searchtype=author&query=Jiao%2C+X">Xiaoran Jiao</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+Z">Zheng Sun</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Hao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+C">Chunhua Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.03141v1-abstract-short" style="display: inline;"> Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes. Previous works approach the problem by inpainting or conditional generation. Both of them can only scaffold motifs with fixed positions, and the conditional generation cannot guarantee the presence of motifs. However… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03141v1-abstract-full').style.display = 'inline'; document.getElementById('2406.03141v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.03141v1-abstract-full" style="display: none;"> Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes. Previous works approach the problem by inpainting or conditional generation. Both of them can only scaffold motifs with fixed positions, and the conditional generation cannot guarantee the presence of motifs. However, prior knowledge of the relative motif positions in a protein is not readily available, and constructing a protein with multiple functions in one protein is more general and significant because of the synergies between functions. We propose a Floating Anchor Diffusion (FADiff) model. FADiff allows motifs to float rigidly and independently in the process of diffusion, which guarantees the presence of motifs and automates the motif position design. Our experiments demonstrate the efficacy of FADiff with high success rates and designable novel scaffolds. To the best of our knowledge, FADiff is the first work to tackle the challenge of scaffolding multiple motifs without relying on the expertise of relative motif positions in the protein. Code is available at https://github.com/aim-uofa/FADiff. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03141v1-abstract-full').style.display = 'none'; document.getElementById('2406.03141v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICML 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.02014">arXiv:2406.02014</a> <span> [<a href="https://arxiv.org/pdf/2406.02014">pdf</a>, <a href="https://arxiv.org/format/2406.02014">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Understanding Auditory Evoked Brain Signal via Physics-informed Embedding Network with Multi-Task Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wanli Ma</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+X">Xuegang Tang</a>, <a href="/search/q-bio?searchtype=author&query=Gu%2C+J">Jin Gu</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Ying Wang</a>, <a href="/search/q-bio?searchtype=author&query=Xia%2C+Y">Yuling Xia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.02014v1-abstract-short" style="display: inline;"> In the fields of brain-computer interaction and cognitive neuroscience, effective decoding of auditory signals from task-based functional magnetic resonance imaging (fMRI) is key to understanding how the brain processes complex auditory information. Although existing methods have enhanced decoding capabilities, limitations remain in information utilization and model representation. To overcome the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02014v1-abstract-full').style.display = 'inline'; document.getElementById('2406.02014v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02014v1-abstract-full" style="display: none;"> In the fields of brain-computer interaction and cognitive neuroscience, effective decoding of auditory signals from task-based functional magnetic resonance imaging (fMRI) is key to understanding how the brain processes complex auditory information. Although existing methods have enhanced decoding capabilities, limitations remain in information utilization and model representation. To overcome these challenges, we propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net), which enhances decoding performance through physics-informed embedding and deep learning techniques. PEMT-Net consists of two principal components: feature augmentation and classification. For feature augmentation, we propose a novel approach by creating neural embedding graphs via node embedding, utilizing random walks to simulate the physical diffusion of neural information. This method captures both local and non-local information overflow and proposes a position encoding based on relative physical coordinates. In the classification segment, we propose adaptive embedding fusion to maximally capture linear and non-linear characteristics. Furthermore, we propose an innovative parameter-sharing mechanism to optimize the retention and learning of extracted features. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding, surpassing existing methods and offering new insights into the brain's mechanisms for processing complex auditory information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02014v1-abstract-full').style.display = 'none'; document.getElementById('2406.02014v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10343">arXiv:2405.10343</a> <span> [<a href="https://arxiv.org/pdf/2405.10343">pdf</a>, <a href="https://arxiv.org/format/2405.10343">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Minghao Li</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yanwen Huang</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhi-Ming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10343v1-abstract-short" style="display: inline;"> Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal model capable of effectively applying to various categories of molecular tasks, since existing prevalent pre-training methods exhibit effectiveness for specific types of downstream tasks. Furthermore, the lack of profound un… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10343v1-abstract-full').style.display = 'inline'; document.getElementById('2405.10343v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10343v1-abstract-full" style="display: none;"> Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal model capable of effectively applying to various categories of molecular tasks, since existing prevalent pre-training methods exhibit effectiveness for specific types of downstream tasks. Furthermore, the lack of profound understanding of existing pre-training methods, including 2D graph masking, 2D-3D contrastive learning, and 3D denoising, hampers the advancement of molecular foundation models. In this work, we provide a unified comprehension of existing pre-training methods through the lens of contrastive learning. Thus their distinctions lie in clustering different views of molecules, which is shown beneficial to specific downstream tasks. To achieve a complete and general-purpose molecular representation, we propose a novel pre-training framework, named UniCorn, that inherits the merits of the three methods, depicting molecular views in three different levels. SOTA performance across quantum, physicochemical, and biological tasks, along with comprehensive ablation study, validate the universality and effectiveness of UniCorn. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10343v1-abstract-full').style.display = 'none'; document.getElementById('2405.10343v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.12141">arXiv:2404.12141</a> <span> [<a href="https://arxiv.org/pdf/2404.12141">pdf</a>, <a href="https://arxiv.org/format/2404.12141">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qu%2C+Y">Yanru Qu</a>, <a href="/search/q-bio?searchtype=author&query=Qiu%2C+K">Keyue Qiu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+J">Jiawei Han</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.12141v4-abstract-short" style="display: inline;"> Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12141v4-abstract-full').style.display = 'inline'; document.getElementById('2404.12141v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.12141v4-abstract-full" style="display: none;"> Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12141v4-abstract-full').style.display = 'none'; document.getElementById('2404.12141v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ICML 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.15441">arXiv:2403.15441</a> <span> [<a href="https://arxiv.org/pdf/2403.15441">pdf</a>, <a href="https://arxiv.org/format/2403.15441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Qu%2C+Y">Yanru Qu</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jingjing Liu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.15441v1-abstract-short" style="display: inline;"> Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geom… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15441v1-abstract-full').style.display = 'inline'; document.getElementById('2403.15441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.15441v1-abstract-full" style="display: none;"> Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87% molecule stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (e.g., 20-times speedup without sacrificing performance). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15441v1-abstract-full').style.display = 'none'; document.getElementById('2403.15441v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICLR 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.14046">arXiv:2403.14046</a> <span> [<a href="https://arxiv.org/pdf/2403.14046">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Desiderata of evidence for representation in neuroscience </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Pohl%2C+S">Stephan Pohl</a>, <a href="/search/q-bio?searchtype=author&query=Walker%2C+E+Y">Edgar Y. Walker</a>, <a href="/search/q-bio?searchtype=author&query=Barack%2C+D+L">David L. Barack</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J">Jennifer Lee</a>, <a href="/search/q-bio?searchtype=author&query=Denison%2C+R+N">Rachel N. Denison</a>, <a href="/search/q-bio?searchtype=author&query=Block%2C+N">Ned Block</a>, <a href="/search/q-bio?searchtype=author&query=Meyniel%2C+F">Florent Meyniel</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.14046v1-abstract-short" style="display: inline;"> This paper develops a systematic framework for the evidence neuroscientists use to establish whether a neural response represents a feature. Researchers try to establish that the neural response is (1) sensitive and (2) specific to the feature, (3) invariant to other features, and (4) functional, which means that it is used downstream in the brain. We formalize these desiderata in information-theo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14046v1-abstract-full').style.display = 'inline'; document.getElementById('2403.14046v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.14046v1-abstract-full" style="display: none;"> This paper develops a systematic framework for the evidence neuroscientists use to establish whether a neural response represents a feature. Researchers try to establish that the neural response is (1) sensitive and (2) specific to the feature, (3) invariant to other features, and (4) functional, which means that it is used downstream in the brain. We formalize these desiderata in information-theoretic terms. This formalism allows us to precisely state the desiderata while unifying the different analysis methods used in neuroscience under one framework. We discuss how common methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence are used to evaluate the desiderata. In doing so, we provide a common terminology to researchers that helps to clarify disagreements, to compare and integrate results across studies and research groups, and to identify when evidence might be missing and when evidence for some representational conclusion is strong. We illustrate the framework with several canonical examples, including the representation of orientation, numerosity, faces, and spatial location. We end by discussing how the framework can be extended to cover models of the neural code, multi-stage models, and other domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14046v1-abstract-full').style.display = 'none'; document.getElementById('2403.14046v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">50 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12995">arXiv:2403.12995</a> <span> [<a href="https://arxiv.org/pdf/2403.12995">pdf</a>, <a href="https://arxiv.org/format/2403.12995">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zheng%2C+K">Kangjie Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Long%2C+S">Siyu Long</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+T">Tianyu Lu</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Junwei Yang</a>, <a href="/search/q-bio?searchtype=author&query=Dai%2C+X">Xinyu Dai</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+M">Ming Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Nie%2C+Z">Zaiqing Nie</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.12995v4-abstract-short" style="display: inline;"> Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small mole… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12995v4-abstract-full').style.display = 'inline'; document.getElementById('2403.12995v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12995v4-abstract-full" style="display: none;"> Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12995v4-abstract-full').style.display = 'none'; document.getElementById('2403.12995v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICML2024 camera-ready, update some experimental results, add github url, fix some typos</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12987">arXiv:2403.12987</a> <span> [<a href="https://arxiv.org/pdf/2403.12987">pdf</a>, <a href="https://arxiv.org/format/2403.12987">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gao%2C+B">Bowen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+M">Minsi Ren</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yanwen Huang</a>, <a href="/search/q-bio?searchtype=author&query=Qiang%2C+B">Bo Qiang</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhi-Ming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.12987v1-abstract-short" style="display: inline;"> In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. T… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12987v1-abstract-full').style.display = 'inline'; document.getElementById('2403.12987v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12987v1-abstract-full" style="display: none;"> In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12987v1-abstract-full').style.display = 'none'; document.getElementById('2403.12987v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.13779">arXiv:2402.13779</a> <span> [<a href="https://arxiv.org/pdf/2402.13779">pdf</a>, <a href="https://arxiv.org/format/2402.13779">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Contextual Molecule Representation Learning from Chemical Reaction Knowledge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tang%2C+H">Han Tang</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+B">Bicheng Lin</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">JIngjing Liu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.13779v1-abstract-short" style="display: inline;"> In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse areas. However, when applied to molecular representation learning (MRL), prevailing techniques such as masked sub-unit reconstruction often fall short, due to the high degree of freedom in the possible combinations of atoms… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13779v1-abstract-full').style.display = 'inline'; document.getElementById('2402.13779v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.13779v1-abstract-full" style="display: none;"> In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse areas. However, when applied to molecular representation learning (MRL), prevailing techniques such as masked sub-unit reconstruction often fall short, due to the high degree of freedom in the possible combinations of atoms within molecules, which brings insurmountable complexity to the masking-reconstruction paradigm. To tackle this challenge, we introduce REMO, a self-supervised learning framework that takes advantage of well-defined atom-combination rules in common chemistry. Specifically, REMO pre-trains graph/Transformer encoders on 1.7 million known chemical reactions in the literature. We propose two pre-training objectives: Masked Reaction Centre Reconstruction (MRCR) and Reaction Centre Identification (RCI). REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge. Such contextual representations can then be utilized to support diverse downstream molecular tasks with minimum finetuning, such as affinity prediction and drug-drug interaction prediction. Extensive experimental results on MoleculeACE, ACNet, drug-drug interaction (DDI), and reaction type classification show that across all tested downstream tasks, REMO outperforms the standard baseline of single-molecule masked modeling used in current MRL. Remarkably, REMO is the pioneering deep learning model surpassing fingerprint-based methods in activity cliff benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13779v1-abstract-full').style.display = 'none'; document.getElementById('2402.13779v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint. Under Review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.16160">arXiv:2311.16160</a> <span> [<a href="https://arxiv.org/pdf/2311.16160">pdf</a>, <a href="https://arxiv.org/format/2311.16160">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Protein-ligand binding representation learning from fine-grained interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Minghao Li</a>, <a href="/search/q-bio?searchtype=author&query=Jia%2C+Y">Yinjun Jia</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.16160v1-abstract-short" style="display: inline;"> The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulting in poor generalization due to limited supervised data. In this paper, we propose to learn protein-ligand binding representation in a self-supervised learning manner. Different f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16160v1-abstract-full').style.display = 'inline'; document.getElementById('2311.16160v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.16160v1-abstract-full" style="display: none;"> The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulting in poor generalization due to limited supervised data. In this paper, we propose to learn protein-ligand binding representation in a self-supervised learning manner. Different from existing pre-training approaches which treat proteins and ligands individually, we emphasize to discern the intricate binding patterns from fine-grained interactions. Specifically, this self-supervised learning problem is formulated as a prediction of the conclusive binding complex structure given a pocket and ligand with a Transformer based interaction module, which naturally emulates the binding process. To ensure the representation of rich binding information, we introduce two pre-training tasks, i.e.~atomic pairwise distance map prediction and mask ligand reconstruction, which comprehensively model the fine-grained interactions from both structure and feature space. Extensive experiments have demonstrated the superiority of our method across various binding tasks, including protein-ligand affinity prediction, virtual screening and protein-ligand docking. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16160v1-abstract-full').style.display = 'none'; document.getElementById('2311.16160v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14077">arXiv:2311.14077</a> <span> [<a href="https://arxiv.org/pdf/2311.14077">pdf</a>, <a href="https://arxiv.org/format/2311.14077">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yiming Wang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Minkai Xu</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+R">Rui Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14077v1-abstract-short" style="display: inline;"> Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task. Inspired by the recent advancements in discrete diffusion models for graph gene… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14077v1-abstract-full').style.display = 'inline'; document.getElementById('2311.14077v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14077v1-abstract-full" style="display: none;"> Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task. Inspired by the recent advancements in discrete diffusion models for graph generation, we introduce Retrosynthesis Diffusion (RetroDiff), a novel diffusion-based method designed to address this problem. However, integrating a diffusion-based graph-to-graph framework while retaining essential chemical reaction template information presents a notable challenge. Our key innovation is to develop a multi-stage diffusion process. In this method, we decompose the retrosynthesis procedure to first sample external groups from the dummy distribution given products and then generate the external bonds to connect the products and generated groups. Interestingly, such a generation process is exactly the reverse of the widely adapted semi-template retrosynthesis procedure, i.e. from reaction center identification to synthon completion, which significantly reduces the error accumulation. Experimental results on the benchmark have demonstrated the superiority of our method over all other semi-template methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14077v1-abstract-full').style.display = 'none'; document.getElementById('2311.14077v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.02124">arXiv:2311.02124</a> <span> [<a href="https://arxiv.org/pdf/2311.02124">pdf</a>, <a href="https://arxiv.org/format/2311.02124">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Sliced Denoising: A Physics-Informed Molecular Pre-Training Method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhi-Ming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.02124v1-abstract-short" style="display: inline;"> While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the learned representation truly captures the underlying explanatory factors in observed data, ultimately resulting in limited generalization and robustness. Although denoising methods offer a physical interpretation, their… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02124v1-abstract-full').style.display = 'inline'; document.getElementById('2311.02124v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.02124v1-abstract-full" style="display: none;"> While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the learned representation truly captures the underlying explanatory factors in observed data, ultimately resulting in limited generalization and robustness. Although denoising methods offer a physical interpretation, their accuracy is often compromised by ad-hoc noise design, leading to inaccurate learned force fields. To address this limitation, this paper proposes a new method for molecular pre-training, called sliced denoising (SliDe), which is based on the classical mechanical intramolecular potential theory. SliDe utilizes a novel noise strategy that perturbs bond lengths, angles, and torsion angles to achieve better sampling over conformations. Additionally, it introduces a random slicing approach that circumvents the computationally expensive calculation of the Jacobian matrix, which is otherwise essential for estimating the force field. By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02124v1-abstract-full').style.display = 'none'; document.getElementById('2311.02124v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.14216">arXiv:2310.14216</a> <span> [<a href="https://arxiv.org/pdf/2310.14216">pdf</a>, <a href="https://arxiv.org/format/2310.14216">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> UniMAP: Universal SMILES-Graph Representation Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+L">Lixin Yang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yanwen Huang</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.14216v2-abstract-short" style="display: inline;"> Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, either SMILES or graph representation. To effectively leverage both modalities, we argue that it is critical to capture the fine-grained 'semantics' between SMILES and graph, because subtle sequence/graph differences may le… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14216v2-abstract-full').style.display = 'inline'; document.getElementById('2310.14216v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.14216v2-abstract-full" style="display: none;"> Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, either SMILES or graph representation. To effectively leverage both modalities, we argue that it is critical to capture the fine-grained 'semantics' between SMILES and graph, because subtle sequence/graph differences may lead to contrary molecular properties. In this paper, we propose a universal SMILE-graph representation learning model, namely UniMAP. Firstly, an embedding layer is employed to obtain the token and node/edge representation in SMILES and graph, respectively. A multi-layer Transformer is then utilized to conduct deep cross-modality fusion. Specially, four kinds of pre-training tasks are designed for UniMAP, including Multi-Level Cross-Modality Masking (CMM), SMILES-Graph Matching (SGM), Fragment-Level Alignment (FLA), and Domain Knowledge Learning (DKL). In this way, both global (i.e. SGM and DKL) and local (i.e. CMM and FLA) alignments are integrated to achieve comprehensive cross-modality fusion. We evaluate UniMAP on various downstream tasks, i.e. molecular property prediction, drug-target affinity prediction and drug-drug interaction. Experimental results show that UniMAP outperforms current state-of-the-art pre-training methods.We also visualize the learned representations to demonstrate the effect of multi-modality integration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14216v2-abstract-full').style.display = 'none'; document.getElementById('2310.14216v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.11802">arXiv:2310.11802</a> <span> [<a href="https://arxiv.org/pdf/2310.11802">pdf</a>, <a href="https://arxiv.org/format/2310.11802">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> De novo protein design using geometric vector field networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Mao%2C+W">Weian Mao</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+M">Muzhi Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+Z">Zheng Sun</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+S">Shuaike Shen</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+L+Y">Lin Yuanbo Wu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Hao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+C">Chunhua Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.11802v1-abstract-short" style="display: inline;"> Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science. These methods typically depend on protein structure encoders to model residue backbone frames, where atoms do not exist. Most prior encoders rely on atom-wise features, such as angles and distances between atoms, which are not available in this context. Thus far,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.11802v1-abstract-full').style.display = 'inline'; document.getElementById('2310.11802v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.11802v1-abstract-full" style="display: none;"> Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science. These methods typically depend on protein structure encoders to model residue backbone frames, where atoms do not exist. Most prior encoders rely on atom-wise features, such as angles and distances between atoms, which are not available in this context. Thus far, only several simple encoders, such as IPA, have been proposed for this scenario, exposing the frame modeling as a bottleneck. In this work, we proffer the Vector Field Network (VFN), which enables network layers to perform learnable vector computations between coordinates of frame-anchored virtual atoms, thus achieving a higher capability for modeling frames. The vector computation operates in a manner similar to a linear layer, with each input channel receiving 3D virtual atom coordinates instead of scalar values. The multiple feature vectors output by the vector computation are then used to update the residue representations and virtual atom coordinates via attention aggregation. Remarkably, VFN also excels in modeling both frames and atoms, as the real atoms can be treated as the virtual atoms for modeling, positioning VFN as a potential universal encoder. In protein diffusion (frame modeling), VFN exhibits an impressive performance advantage over IPA, excelling in terms of both designability (67.04% vs. 53.58%) and diversity (66.54% vs. 51.98%). In inverse folding (frame and atom modeling), VFN outperforms the previous SoTA model, PiFold (54.7% vs. 51.66%), on sequence recovery rate. We also propose a method of equipping VFN with the ESM model, which significantly surpasses the previous ESM-based SoTA (62.67% vs. 55.65%), LM-Design, by a substantial margin. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.11802v1-abstract-full').style.display = 'none'; document.getElementById('2310.11802v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.10683">arXiv:2307.10683</a> <span> [<a href="https://arxiv.org/pdf/2307.10683">pdf</a>, <a href="https://arxiv.org/format/2307.10683">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Fractional Denoising for 3D Molecular Pre-training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Feng%2C+S">Shikun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Ni%2C+Y">Yuyan Ni</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhi-Ming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.10683v3-abstract-short" style="display: inline;"> Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.10683v3-abstract-full').style.display = 'inline'; document.getElementById('2307.10683v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.10683v3-abstract-full" style="display: none;"> Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.10683v3-abstract-full').style.display = 'none'; document.getElementById('2307.10683v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.13266">arXiv:2305.13266</a> <span> [<a href="https://arxiv.org/pdf/2305.13266">pdf</a>, <a href="https://arxiv.org/format/2305.13266">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qiang%2C+B">Bo Qiang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Minkai Xu</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+B">Bowen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.13266v2-abstract-short" style="display: inline;"> Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.13266v2-abstract-full').style.display = 'inline'; document.getElementById('2305.13266v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.13266v2-abstract-full" style="display: none;"> Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a promising strategy, however, it is nontrivial to be adapted for 3D non-autoregressive generations because of the combinational optimization problems. In this paper, we utilize a coarse-to-fine strategy to tackle this problem, in which a Hierarchical Diffusion-based model (i.e.~HierDiff) is proposed to preserve the validity of local segments without relying on autoregressive modeling. Specifically, HierDiff first generates coarse-grained molecule geometries via an equivariant diffusion process, where each coarse-grained node reflects a fragment in a molecule. Then the coarse-grained nodes are decoded into fine-grained fragments by a message-passing process and a newly designed iterative refined sampling module. Lastly, the fine-grained fragments are then assembled to derive a complete atomic molecular structure. Extensive experiments demonstrate that HierDiff consistently improves the quality of molecule generation over existing methods <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.13266v2-abstract-full').style.display = 'none'; document.getElementById('2305.13266v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICML 2023 poster</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.01978">arXiv:2211.01978</a> <span> [<a href="https://arxiv.org/pdf/2211.01978">pdf</a>, <a href="https://arxiv.org/format/2211.01978">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3511808.3557142">10.1145/3511808.3557142 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yuancheng Sun</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yimeng Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weizhi Ma</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+W">Wenhao Huang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+K">Kang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhiming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.01978v1-abstract-short" style="display: inline;"> Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.01978v1-abstract-full').style.display = 'inline'; document.getElementById('2211.01978v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.01978v1-abstract-full" style="display: none;"> Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.01978v1-abstract-full').style.display = 'none'; document.getElementById('2211.01978v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages. Published in CIKM 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.14195">arXiv:2205.14195</a> <span> [<a href="https://arxiv.org/pdf/2205.14195">pdf</a>, <a href="https://arxiv.org/format/2205.14195">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised learning of features and object boundaries from local prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sch%C3%BCtt%2C+H+H">Heiko H. Sch眉tt</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.14195v1-abstract-short" style="display: inline;"> A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature maps with a pairwise Markov random field model in which each factor is paired with… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14195v1-abstract-full').style.display = 'inline'; document.getElementById('2205.14195v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.14195v1-abstract-full" style="display: none;"> A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature maps with a pairwise Markov random field model in which each factor is paired with an additional binary variable, which switches the factor on or off. Using one of two contrastive learning objectives, we can learn both the features and the parameters of the Markov random field factors from images without further supervision signals. The features learned by shallow neural networks based on this loss are local averages, opponent colors, and Gabor-like stripe patterns. Furthermore, we can infer connectivity between locations by inferring the switch variables. Contours inferred from this connectivity perform quite well on the Berkeley segmentation database (BSDS500) without any training on contours. Thus, computing predictions across space aids both segmentation and feature learning, and models trained to optimize these predictions show similarities to the human visual system. We speculate that retinotopic visual cortex might implement such predictions over space through lateral connections. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14195v1-abstract-full').style.display = 'none'; document.getElementById('2205.14195v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to NeurIPS 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.04324">arXiv:2202.04324</a> <span> [<a href="https://arxiv.org/pdf/2202.04324">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41593-023-01444-y">10.1038/s41593-023-01444-y <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Studying the neural representations of uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Walker%2C+E+Y">Edgar Y Walker</a>, <a href="/search/q-bio?searchtype=author&query=Pohl%2C+S">Stephan Pohl</a>, <a href="/search/q-bio?searchtype=author&query=Denison%2C+R+N">Rachel N Denison</a>, <a href="/search/q-bio?searchtype=author&query=Barack%2C+D+L">David L Barack</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J">Jennifer Lee</a>, <a href="/search/q-bio?searchtype=author&query=Block%2C+N">Ned Block</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a>, <a href="/search/q-bio?searchtype=author&query=Meyniel%2C+F">Florent Meyniel</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.04324v4-abstract-short" style="display: inline;"> The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish betw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04324v4-abstract-full').style.display = 'inline'; document.getElementById('2202.04324v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.04324v4-abstract-full" style="display: none;"> The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between "code-driven" and "correlational" approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance, functionality. Our analysis reveals that the two approaches lead to different, but complementary findings, shaping new research questions and guiding future experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04324v4-abstract-full').style.display = 'none'; document.getElementById('2202.04324v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 3 figures. Nature Neuroscience (2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.10918">arXiv:2110.10918</a> <span> [<a href="https://arxiv.org/pdf/2110.10918">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei Ma</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+Q">Qin Xie</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jianhang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+S">Shiliang Li</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+Y">Youjun Xu</a>, <a href="/search/q-bio?searchtype=author&query=Deng%2C+X">Xiaobing Deng</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+W">Weilin Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.10918v2-abstract-short" style="display: inline;"> Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.10918v2-abstract-full').style.display = 'inline'; document.getElementById('2110.10918v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.10918v2-abstract-full" style="display: none;"> Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.10918v2-abstract-full').style.display = 'none'; document.getElementById('2110.10918v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.12955">arXiv:2104.12955</a> <span> [<a href="https://arxiv.org/pdf/2104.12955">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> Local vaccination and systemic tumor suppression via irradiation and manganese adjuvant in mice </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lu%2C+C">Chunyang Lu</a>, <a href="/search/q-bio?searchtype=author&query=Qian%2C+J">Jing Qian</a>, <a href="/search/q-bio?searchtype=author&query=Lv%2C+J">Jianfeng Lv</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+J">Jintao Han</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+X">Xiaoyi Sun</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junyi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+S">Siwei Ding</a>, <a href="/search/q-bio?searchtype=author&query=Mei%2C+Z">Zhusong Mei</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+Y">Yulan Liang</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Y">Yuqi Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Ye Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+C">Chen Lin</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yanying Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Geng%2C+Y">Yixing Geng</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wenjun Ma</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yugang Wang</a>, <a href="/search/q-bio?searchtype=author&query=Yan%2C+X">Xueqing Yan</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+G">Gen Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.12955v1-abstract-short" style="display: inline;"> Presently 4T-1 luc cells were irradiated with proton under ultra-high dose rate FLASH or with gamma-ray with conventional dose rate, and then subcutaneous vaccination with or without Mn immuno-enhancing adjuvant into the mice for three times. One week later, we injected untreated 4T-1 luc cells on the other side of the vaccinated mice, and found that the untreated 4T-1 luc cells injected later nea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.12955v1-abstract-full').style.display = 'inline'; document.getElementById('2104.12955v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.12955v1-abstract-full" style="display: none;"> Presently 4T-1 luc cells were irradiated with proton under ultra-high dose rate FLASH or with gamma-ray with conventional dose rate, and then subcutaneous vaccination with or without Mn immuno-enhancing adjuvant into the mice for three times. One week later, we injected untreated 4T-1 luc cells on the other side of the vaccinated mice, and found that the untreated 4T-1 luc cells injected later nearly totally did not grow tumor (1/17) while controls without previous vaccination all grow tumors (18/18). The result is very interesting and the findings may help to explore in situ tumor vaccination as well as new combined radiotherapy strategies to effectively ablate primary and disseminated tumors. To our limited knowledge, this is the first paper reporting the high efficiency induction of systemic vaccination suppressing the metastasized/disseminated tumor progression. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.12955v1-abstract-full').style.display = 'none'; document.getElementById('2104.12955v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 3 figures and 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.02181">arXiv:2005.02181</a> <span> [<a href="https://arxiv.org/pdf/2005.02181">pdf</a>, <a href="https://arxiv.org/format/2005.02181">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> A neural network walks into a lab: towards using deep nets as models for human behavior </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a>, <a href="/search/q-bio?searchtype=author&query=Peters%2C+B">Benjamin Peters</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.02181v1-abstract-short" style="display: inline;"> What might sound like the beginning of a joke has become an attractive prospect for many cognitive scientists: the use of deep neural network models (DNNs) as models of human behavior in perceptual and cognitive tasks. Although DNNs have taken over machine learning, attempts to use them as models of human behavior are still in the early stages. Can they become a versatile model class in the cognit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.02181v1-abstract-full').style.display = 'inline'; document.getElementById('2005.02181v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.02181v1-abstract-full" style="display: none;"> What might sound like the beginning of a joke has become an attractive prospect for many cognitive scientists: the use of deep neural network models (DNNs) as models of human behavior in perceptual and cognitive tasks. Although DNNs have taken over machine learning, attempts to use them as models of human behavior are still in the early stages. Can they become a versatile model class in the cognitive scientist's toolbox? We first argue why DNNs have the potential to be interesting models of human behavior. We then discuss how that potential can be more fully realized. On the one hand, we argue that the cycle of training, testing, and revising DNNs needs to be revisited through the lens of the cognitive scientist's goals. Specifically, we argue that methods for assessing the goodness of fit between DNN models and human behavior have to date been impoverished. On the other hand, cognitive science might have to start using more complex tasks (including richer stimulus spaces), but doing so might be beneficial for DNN-independent reasons as well. Finally, we highlight avenues where traditional cognitive process models and DNNs may show productive synergy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.02181v1-abstract-full').style.display = 'none'; document.getElementById('2005.02181v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.03985">arXiv:2001.03985</a> <span> [<a href="https://arxiv.org/pdf/2001.03985">pdf</a>, <a href="https://arxiv.org/format/2001.03985">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pcbi.1008483">10.1371/journal.pcbi.1008483 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=van+Opheusden%2C+B">Bas van Opheusden</a>, <a href="/search/q-bio?searchtype=author&query=Acerbi%2C+L">Luigi Acerbi</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.03985v3-abstract-short" style="display: inline;"> The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function. The log-likelihood is the key element for parameter estimation and model evaluation. However, the log-likelihood of complex models in fields such as computational biology and neuroscience is often intractable to compute… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.03985v3-abstract-full').style.display = 'inline'; document.getElementById('2001.03985v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.03985v3-abstract-full" style="display: none;"> The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function. The log-likelihood is the key element for parameter estimation and model evaluation. However, the log-likelihood of complex models in fields such as computational biology and neuroscience is often intractable to compute analytically or numerically. In those cases, researchers can often only estimate the log-likelihood by comparing observed data with synthetic observations generated by model simulations. Standard techniques to approximate the likelihood via simulation either use summary statistics of the data or are at risk of producing severe biases in the estimate. Here, we explore another method, inverse binomial sampling (IBS), which can estimate the log-likelihood of an entire data set efficiently and without bias. For each observation, IBS draws samples from the simulator model until one matches the observation. The log-likelihood estimate is then a function of the number of samples drawn. The variance of this estimator is uniformly bounded, achieves the minimum variance for an unbiased estimator, and we can compute calibrated estimates of the variance. We provide theoretical arguments in favor of IBS and an empirical assessment of the method for maximum-likelihood estimation with simulation-based models. As case studies, we take three model-fitting problems of increasing complexity from computational and cognitive neuroscience. In all problems, IBS generally produces lower error in the estimated parameters and maximum log-likelihood values than alternative sampling methods with the same average number of samples. Our results demonstrate the potential of IBS as a practical, robust, and easy to implement method for log-likelihood evaluation when exact techniques are not available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.03985v3-abstract-full').style.display = 'none'; document.getElementById('2001.03985v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Bas van Opheusden and Luigi Acerbi contributed equally to this work</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.11640">arXiv:1905.11640</a> <span> [<a href="https://arxiv.org/pdf/1905.11640">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> AmoebaContact and GDFold: a new pipeline for rapid prediction of protein structures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Mao%2C+W">Wenzhi Mao</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+W">Wenze Ding</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+H">Haipeng Gong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.11640v1-abstract-short" style="display: inline;"> Native contacts between residues could be predicted from the amino acid sequence of proteins, and the predicted contact information could assist the de novo protein structure prediction. Here, we present a novel pipeline of a residue contact predictor AmoebaContact and a contact-assisted folder GDFold for rapid protein structure prediction. Unlike mainstream contact predictors that utilize human-d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.11640v1-abstract-full').style.display = 'inline'; document.getElementById('1905.11640v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.11640v1-abstract-full" style="display: none;"> Native contacts between residues could be predicted from the amino acid sequence of proteins, and the predicted contact information could assist the de novo protein structure prediction. Here, we present a novel pipeline of a residue contact predictor AmoebaContact and a contact-assisted folder GDFold for rapid protein structure prediction. Unlike mainstream contact predictors that utilize human-designed neural networks, AmoebaContact adopts a set of network architectures that are found as optimal for contact prediction through automatic searching and predicts the residue contacts at a series of cutoffs. Different from conventional contact-assisted folders that only use top-scored contact pairs, GDFold considers all residue pairs from the prediction results of AmoebaContact in a differentiable loss function and optimizes the atom coordinates using the gradient descent algorithm. Combination of AmoebaContact and GDFold allows quick reconstruction of the protein structure, with comparable model quality to the state-of-the-art protein structure prediction methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.11640v1-abstract-full').style.display = 'none'; document.getElementById('1905.11640v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.06456">arXiv:1802.06456</a> <span> [<a href="https://arxiv.org/pdf/1802.06456">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Optimal allocation of attentional resource to multiple items with unequal relevance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=de+Silva%2C+N">Nuwan de Silva</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.06456v1-abstract-short" style="display: inline;"> In natural perception, different items (objects) in a scene are rarely equally relevant to the observer. The brain improves performance by directing attention to the most relevant items, for example the ones most likely to be probed. For a general set of probing probabilities, it is not known how attentional resources should be allocated to maximize performance. Here, we investigate the optimal st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.06456v1-abstract-full').style.display = 'inline'; document.getElementById('1802.06456v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.06456v1-abstract-full" style="display: none;"> In natural perception, different items (objects) in a scene are rarely equally relevant to the observer. The brain improves performance by directing attention to the most relevant items, for example the ones most likely to be probed. For a general set of probing probabilities, it is not known how attentional resources should be allocated to maximize performance. Here, we investigate the optimal strategy for allocating a fixed resource budget E among N items when on each trial, only one item gets probed. We develop an efficient algorithm that, for any concave utility function, reduces the N-dimensional problem to a set of N one-dimensional problems that the brain could plausibly solve. We find that the intuitive strategy of allocating resource in proportion to the probing probabilities is in general not optimal. In particular, in some tasks, if resource is low, the optimal strategy involves allocating zero resource to items with a nonzero probability of being probed. Our work opens the door to normatively guided studies of attentional allocation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.06456v1-abstract-full').style.display = 'none'; document.getElementById('1802.06456v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.06144">arXiv:1705.06144</a> <span> [<a href="https://arxiv.org/pdf/1705.06144">pdf</a>, <a href="https://arxiv.org/ps/1705.06144">ps</a>, <a href="https://arxiv.org/format/1705.06144">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.physa.2018.05.118">10.1016/j.physa.2018.05.118 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Can morphological changes of erythrocytes be driven by hemoglobin? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gevorkian%2C+S+G">S. G. Gevorkian</a>, <a href="/search/q-bio?searchtype=author&query=Allahverdyan%2C+A+E">A. E. Allahverdyan</a>, <a href="/search/q-bio?searchtype=author&query=Gevorgyan%2C+S">S. Gevorgyan</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wen-Jong Ma</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+C">Chin-Kun Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.06144v1-abstract-short" style="display: inline;"> At 49 C erythrocytes undergo morphological changes due to an internal force, but the origin of the force that drives changes is not clear. Here we point out that our recent experiments on thermally induced force-release in hemoglobin can provide an explanation for the morphological changes of erythrocytes. </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.06144v1-abstract-full" style="display: none;"> At 49 C erythrocytes undergo morphological changes due to an internal force, but the origin of the force that drives changes is not clear. Here we point out that our recent experiments on thermally induced force-release in hemoglobin can provide an explanation for the morphological changes of erythrocytes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.06144v1-abstract-full').style.display = 'none'; document.getElementById('1705.06144v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.04405">arXiv:1705.04405</a> <span> [<a href="https://arxiv.org/pdf/1705.04405">pdf</a>, <a href="https://arxiv.org/format/1705.04405">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Acerbi%2C+L">Luigi Acerbi</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.04405v2-abstract-short" style="display: inline;"> Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.04405v2-abstract-full').style.display = 'inline'; document.getElementById('1705.04405v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.04405v2-abstract-full" style="display: none;"> Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.04405v2-abstract-full').style.display = 'none'; document.getElementById('1705.04405v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017). 21 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1601.03060">arXiv:1601.03060</a> <span> [<a href="https://arxiv.org/pdf/1601.03060">pdf</a>, <a href="https://arxiv.org/format/1601.03060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Orhan%2C+A+E">A. Emin Orhan</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1601.03060v4-abstract-short" style="display: inline;"> Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.03060v4-abstract-full').style.display = 'inline'; document.getElementById('1601.03060v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.03060v4-abstract-full" style="display: none;"> Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.03060v4-abstract-full').style.display = 'none'; document.getElementById('1601.03060v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages, 10 figures, 6 supplementary figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1503.01216">arXiv:1503.01216</a> <span> [<a href="https://arxiv.org/pdf/1503.01216">pdf</a>, <a href="https://arxiv.org/format/1503.01216">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Visual Decisions in the Presence of Measurement and Stimulus Correlations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bhardwaj%2C+M">Manisha Bhardwaj</a>, <a href="/search/q-bio?searchtype=author&query=Carroll%2C+S">Sam Carroll</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a>, <a href="/search/q-bio?searchtype=author&query=Josic%2C+K">Kresimir Josic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1503.01216v1-abstract-short" style="display: inline;"> Humans and other animals base their decisions on noisy sensory input. Much work has therefore been devoted to understanding the computations that underly such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, the impact of correlations in sensory noise on perceptual judgments is not well understood. Here we examine how stimulus correla… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.01216v1-abstract-full').style.display = 'inline'; document.getElementById('1503.01216v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1503.01216v1-abstract-full" style="display: none;"> Humans and other animals base their decisions on noisy sensory input. Much work has therefore been devoted to understanding the computations that underly such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, the impact of correlations in sensory noise on perceptual judgments is not well understood. Here we examine how stimulus correlations together with correlations in sensory noise impact decision making. As an example, we consider the task of detecting the presence of a single or multiple targets amongst distractors. We assume that both the distractors and the observer's measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial, yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact, unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore strongly impact perceptual judgments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.01216v1-abstract-full').style.display = 'none'; document.getElementById('1503.01216v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1410.1093">arXiv:1410.1093</a> <span> [<a href="https://arxiv.org/pdf/1410.1093">pdf</a>, <a href="https://arxiv.org/ps/1410.1093">ps</a>, <a href="https://arxiv.org/format/1410.1093">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Neural Population Coding of Multiple Stimuli </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Orhan%2C+A+E">A. Emin Orhan</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W+J">Wei Ji Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1410.1093v2-abstract-short" style="display: inline;"> In natural scenes, objects generally appear together with other objects. Yet, theoretical studies of neural population coding typically focus on the encoding of single objects in isolation. Experimental studies suggest that neural responses to multiple objects are well described by linear or nonlinear combinations of the responses to constituent objects, a phenomenon we call stimulus mixing. Here,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.1093v2-abstract-full').style.display = 'inline'; document.getElementById('1410.1093v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1410.1093v2-abstract-full" style="display: none;"> In natural scenes, objects generally appear together with other objects. Yet, theoretical studies of neural population coding typically focus on the encoding of single objects in isolation. Experimental studies suggest that neural responses to multiple objects are well described by linear or nonlinear combinations of the responses to constituent objects, a phenomenon we call stimulus mixing. Here, we present a theoretical analysis of the consequences of common forms of stimulus mixing observed in cortical responses. We show that some of these mixing rules can severely compromise the brain's ability to decode the individual objects. This cost is usually greater than the cost incurred by even large reductions in the gain or large increases in neural variability, explaining why the benefits of attention can be understood primarily in terms of a stimulus selection, or demixing, mechanism rather than purely as a gain increase or noise reduction mechanism. The cost of stimulus mixing becomes even higher when the number of encoded objects increases, suggesting a novel mechanism that might contribute to set size effects observed in myriad psychophysical tasks. We further show that a specific form of neural correlation and heterogeneity in stimulus mixing among the neurons can partially alleviate the harmful effects of stimulus mixing. Finally, we derive simple conditions that must be satisfied for unharmful mixing of stimuli. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.1093v2-abstract-full').style.display = 'none'; document.getElementById('1410.1093v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 December, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 October, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">43 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1203.0081">arXiv:1203.0081</a> <span> [<a href="https://arxiv.org/pdf/1203.0081">pdf</a>, <a href="https://arxiv.org/ps/1203.0081">ps</a>, <a href="https://arxiv.org/format/1203.0081">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1209/0295-5075/96/68005">10.1209/0295-5075/96/68005 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Universal geometrical factor of protein conformations as a consequence of energy minimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wu%2C+M">Ming-Chya Wu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M+S">Mai Suan Li</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wen-Jong Ma</a>, <a href="/search/q-bio?searchtype=author&query=Kouza%2C+M">Maksim Kouza</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+C">Chin-Kun Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1203.0081v1-abstract-short" style="display: inline;"> The biological activity and functional specificity of proteins depend on their native three-dimensional structures determined by inter- and intra-molecular interactions. In this paper, we investigate the geometrical factor of protein conformation as a consequence of energy minimization in protein folding. Folding simulations of 10 polypeptides with chain length ranging from 183 to 548 residues man… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1203.0081v1-abstract-full').style.display = 'inline'; document.getElementById('1203.0081v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1203.0081v1-abstract-full" style="display: none;"> The biological activity and functional specificity of proteins depend on their native three-dimensional structures determined by inter- and intra-molecular interactions. In this paper, we investigate the geometrical factor of protein conformation as a consequence of energy minimization in protein folding. Folding simulations of 10 polypeptides with chain length ranging from 183 to 548 residues manifest that the dimensionless ratio (V/(A<r>)) of the van der Waals volume V to the surface area A and average atomic radius <r> of the folded structures, calculated with atomic radii setting used in SMMP [Eisenmenger F., et. al., Comput. Phys. Commun., 138 (2001) 192], approach 0.49 quickly during the course of energy minimization. A large scale analysis of protein structures show that the ratio for real and well-designed proteins is universal and equal to 0.491\pm0.005. The fractional composition of hydrophobic and hydrophilic residues does not affect the ratio substantially. The ratio also holds for intrinsically disordered proteins, while it ceases to be universal for polypeptides with bad folding properties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1203.0081v1-abstract-full').style.display = 'none'; document.getElementById('1203.0081v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 February, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 1 table, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> EPL - Europhys. Lett. 96, 68005 (2011) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/q-bio/0610028">arXiv:q-bio/0610028</a> <span> [<a href="https://arxiv.org/pdf/q-bio/0610028">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> </div> <p class="title is-5 mathjax"> Robustness and modular design of the Drosophila segment polarity network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wenzhe Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+L">Luhua Lai</a>, <a href="/search/q-bio?searchtype=author&query=Ouyang%2C+Q">Qi Ouyang</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+C">Chao Tang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="q-bio/0610028v2-abstract-short" style="display: inline;"> Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that while the vast majority of topologies can either not perform the required function o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0610028v2-abstract-full').style.display = 'inline'; document.getElementById('q-bio/0610028v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="q-bio/0610028v2-abstract-full" style="display: none;"> Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that while the vast majority of topologies can either not perform the required function or only do so very fragilely, a small fraction of topologies emerges as particularly robust for the function. The topology adopted by Drosophila, that of the segment polarity network, is a top ranking one among all topologies with no direct autoregulation. Furthermore, we found that all robust topologies are modular--each being a combination of three kinds of modules. These modules can be traced back to three sub-functions of the patterning function and their combinations provide a combinatorial variability for the robust topologies. Our results suggest that the requirement of functional robustness drastically reduces the choices of viable topology to a limited set of modular combinations among which nature optimizes its choice under evolutionary and other biological constraints. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0610028v2-abstract-full').style.display = 'none'; document.getElementById('q-bio/0610028v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2006; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 October, 2006; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2006. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Supplementary Information and Synopsis available at http://www.ucsf.edu/tanglab/</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Molecular Systems Biology 2:70 (2006). </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/q-bio/0505037">arXiv:q-bio/0505037</a> <span> [<a href="https://arxiv.org/pdf/q-bio/0505037">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1529/biophysj.104.057158">10.1529/biophysj.104.057158 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Specificity of Trypsin and Chymotrypsin: Loop Motion Controlled Dynamic Correlation as a Determinant </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wenzhe Ma</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+C">Chao Tang</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+L">Luhua Lai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="q-bio/0505037v1-abstract-short" style="display: inline;"> Trypsin and chymotrypsin are both serine proteases with high sequence and structural similarities, but with different substrate specificity. Previous experiments have demonstrated the critical role of the two loops outside the binding pocket in controlling the specificity of the two enzymes. To understand the mechanism of such a control of specificity by distant loops, we have used the Gaussian… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0505037v1-abstract-full').style.display = 'inline'; document.getElementById('q-bio/0505037v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="q-bio/0505037v1-abstract-full" style="display: none;"> Trypsin and chymotrypsin are both serine proteases with high sequence and structural similarities, but with different substrate specificity. Previous experiments have demonstrated the critical role of the two loops outside the binding pocket in controlling the specificity of the two enzymes. To understand the mechanism of such a control of specificity by distant loops, we have used the Gaussian Network Model to study the dynamic properties of trypsin and chymotrypsin and the roles played by the two loops. A clustering method was introduced to analyze the correlated motions of residues. We have found that trypsin and chymotrypsin have distinct dynamic signatures in the two loop regions which are in turn highly correlated with motions of certain residues in the binding pockets. Interestingly, replacing the two loops of trypsin with those of chymotrypsin changes the motion style of trypsin to chymotrypsin-like, whereas the same experimental replacement was shown necessary to make trypsin have chymotrypsin's enzyme specificity and activity. These results suggest that the cooperative motions of the two loops and the substrate-binding sites contribute to the activity and substrate specificity of trypsin and chymotrypsin. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0505037v1-abstract-full').style.display = 'none'; document.getElementById('q-bio/0505037v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2005; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2005. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">41 pages, 7 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 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