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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/2409.12215">arXiv:2409.12215</a> <span> [<a href="https://arxiv.org/pdf/2409.12215">pdf</a>, <a href="https://arxiv.org/format/2409.12215">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"> Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Overbeek%2C+J+C">Jamie C. Overbeek</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T+S">Thomas S. Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Chia%2C+N">Nicholas Chia</a>, <a href="/search/q-bio?searchtype=author&query=Narykov%2C+O">Oleksandr Narykov</a>, <a href="/search/q-bio?searchtype=author&query=Vasanthakumari%2C+P">Priyanka Vasanthakumari</a>, <a href="/search/q-bio?searchtype=author&query=Wilke%2C+A">Andreas Wilke</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yitan Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Jones%2C+S">Sara Jones</a>, <a href="/search/q-bio?searchtype=author&query=Gnanaolivu%2C+R">Rohan Gnanaolivu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Y">Yuanhang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+J">Jun Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+C">Chen Wang</a>, <a href="/search/q-bio?searchtype=author&query=Knutson%2C+C">Carter Knutson</a>, <a href="/search/q-bio?searchtype=author&query=McNaughton%2C+A">Andrew McNaughton</a>, <a href="/search/q-bio?searchtype=author&query=Kumar%2C+N">Neeraj Kumar</a>, <a href="/search/q-bio?searchtype=author&query=Fernando%2C+G+D">Gayara Demini Fernando</a>, <a href="/search/q-bio?searchtype=author&query=Ghosh%2C+S">Souparno Ghosh</a>, <a href="/search/q-bio?searchtype=author&query=Sanchez-Villalobos%2C+C">Cesar Sanchez-Villalobos</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+R">Ruibo Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Pal%2C+R">Ranadip Pal</a>, <a href="/search/q-bio?searchtype=author&query=Weil%2C+M+R">M. Ryan Weil</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R+L">Rick L. Stevens</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="2409.12215v1-abstract-short" style="display: inline;"> Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collabor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12215v1-abstract-full').style.display = 'inline'; document.getElementById('2409.12215v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.12215v1-abstract-full" style="display: none;"> Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12215v1-abstract-full').style.display = 'none'; document.getElementById('2409.12215v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">12 pages, 2 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/2308.01921">arXiv:2308.01921</a> <span> [<a href="https://arxiv.org/pdf/2308.01921">pdf</a>, <a href="https://arxiv.org/format/2308.01921">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"> Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+W">Wei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+Y">Yihui Ren</a>, <a href="/search/q-bio?searchtype=author&query=Kagawa%2C+A">Ai Kagawa</a>, <a href="/search/q-bio?searchtype=author&query=Carbone%2C+M+R">Matthew R. Carbone</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+S+Y">Samuel Yen-Chi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Qu%2C+X">Xiaohui Qu</a>, <a href="/search/q-bio?searchtype=author&query=Yoo%2C+S">Shinjae Yoo</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Ramanathan%2C+A">Arvind Ramanathan</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R+L">Rick L. Stevens</a>, <a href="/search/q-bio?searchtype=author&query=van+Dam%2C+H+J+J">Hubertus J. J. van Dam</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+D">Deyu Lu</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="2308.01921v3-abstract-short" style="display: inline;"> Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.01921v3-abstract-full').style.display = 'inline'; document.getElementById('2308.01921v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.01921v3-abstract-full" style="display: none;"> Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.01921v3-abstract-full').style.display = 'none'; document.getElementById('2308.01921v3-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 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">8 pages, 5 figures, 2 tables, accepted by ICLMA2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.10442">arXiv:2211.10442</a> <span> [<a href="https://arxiv.org/pdf/2211.10442">pdf</a>, <a href="https://arxiv.org/format/2211.10442">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> </div> </div> <p class="title is-5 mathjax"> Deep learning methods for drug response prediction in cancer: predominant and emerging trends </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T+S">Thomas S. Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yitan Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Narykov%2C+O">Oleksandr Narykov</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Overbeek%2C+J">Jamie Overbeek</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R+L">Rick L. Stevens</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="2211.10442v1-abstract-short" style="display: inline;"> Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10442v1-abstract-full').style.display = 'inline'; document.getElementById('2211.10442v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.10442v1-abstract-full" style="display: none;"> Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 60 deep learning-based models have been curated and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10442v1-abstract-full').style.display = 'none'; document.getElementById('2211.10442v1-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.05012">arXiv:2109.05012</a> <span> [<a href="https://arxiv.org/pdf/2109.05012">pdf</a>, <a href="https://arxiv.org/format/2109.05012">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="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Shah%2C+A">Ashka Shah</a>, <a href="/search/q-bio?searchtype=author&query=Zvyagin%2C+M">Max Zvyagin</a>, <a href="/search/q-bio?searchtype=author&query=Ramanathan%2C+A">Arvind Ramanathan</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2109.05012v1-abstract-short" style="display: inline;"> Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SARS-CoV-2 virtual screening studies and JAK2 kinase assay data, we showcase how a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.05012v1-abstract-full').style.display = 'inline'; document.getElementById('2109.05012v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.05012v1-abstract-full" style="display: none;"> Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SARS-CoV-2 virtual screening studies and JAK2 kinase assay data, we showcase how a scaffold based conception of chemical space is intuitive for design. Lastly, we highlight the utility of scaffold based networks for chemical space as a potential solution to the intractable enumeration problem of chemical space by working inductively on local neighborhoods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.05012v1-abstract-full').style.display = 'none'; document.getElementById('2109.05012v1-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> 10 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.07036">arXiv:2106.07036</a> <span> [<a href="https://arxiv.org/pdf/2106.07036">pdf</a>, <a href="https://arxiv.org/format/2106.07036">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 Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Thomas Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Yoo%2C+H">Hyunseung Yoo</a>, <a href="/search/q-bio?searchtype=author&query=Babuji%2C+Y">Yadu Babuji</a>, <a href="/search/q-bio?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/q-bio?searchtype=author&query=Merzky%2C+A">Andre Merzky</a>, <a href="/search/q-bio?searchtype=author&query=Turilli%2C+M">Matteo Turilli</a>, <a href="/search/q-bio?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/q-bio?searchtype=author&query=Ramanathan%2C+A">Arvind Ramanathan</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2106.07036v2-abstract-short" style="display: inline;"> We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. Our work shows surrogate docking models have six orders of magnitude more throughput than standa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07036v2-abstract-full').style.display = 'inline'; document.getElementById('2106.07036v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.07036v2-abstract-full" style="display: none;"> We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. Our work shows surrogate docking models have six orders of magnitude more throughput than standard docking protocols on the same supercomputer node types. We demonstrate the power of high-speed surrogate models by running each target against 1 billion molecules in under a day (50k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate ML models as a pre-filter. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01\% of detecting the underlying best scoring 0.1\% of compounds. Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules). We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100x or even 1000x faster than current techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07036v2-abstract-full').style.display = 'none'; document.getElementById('2106.07036v2-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 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.02190">arXiv:2106.02190</a> <span> [<a href="https://arxiv.org/pdf/2106.02190">pdf</a>, <a href="https://arxiv.org/format/2106.02190">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"> Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wu%2C+Y">Yulun Wu</a>, <a href="/search/q-bio?searchtype=author&query=Cashman%2C+M">Mikaela Cashman</a>, <a href="/search/q-bio?searchtype=author&query=Choma%2C+N">Nicholas Choma</a>, <a href="/search/q-bio?searchtype=author&query=Prates%2C+%C3%89+T">脡rica T. Prates</a>, <a href="/search/q-bio?searchtype=author&query=Vergara%2C+V+G+M">Ver贸nica G. Melesse Vergara</a>, <a href="/search/q-bio?searchtype=author&query=Shah%2C+M">Manesh Shah</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+A">Andrew Chen</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T+S">Thomas S. Brettin</a>, <a href="/search/q-bio?searchtype=author&query=de+Jong%2C+W+A">Wibe A. de Jong</a>, <a href="/search/q-bio?searchtype=author&query=Kumar%2C+N">Neeraj Kumar</a>, <a href="/search/q-bio?searchtype=author&query=Head%2C+M+S">Martha S. Head</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R+L">Rick L. Stevens</a>, <a href="/search/q-bio?searchtype=author&query=Nugent%2C+P">Peter Nugent</a>, <a href="/search/q-bio?searchtype=author&query=Jacobson%2C+D+A">Daniel A. Jacobson</a>, <a href="/search/q-bio?searchtype=author&query=Brown%2C+J+B">James B. Brown</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="2106.02190v6-abstract-short" style="display: inline;"> We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. W… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.02190v6-abstract-full').style.display = 'inline'; document.getElementById('2106.02190v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.02190v6-abstract-full" style="display: none;"> We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure -- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.02190v6-abstract-full').style.display = 'none'; document.getElementById('2106.02190v6-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.08961">arXiv:2104.08961</a> <span> [<a href="https://arxiv.org/pdf/2104.08961">pdf</a>, <a href="https://arxiv.org/format/2104.08961">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> </div> </div> <p class="title is-5 mathjax"> A cross-study analysis of drug response prediction in cancer cell lines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xia%2C+F">Fangfang Xia</a>, <a href="/search/q-bio?searchtype=author&query=Allen%2C+J">Jonathan Allen</a>, <a href="/search/q-bio?searchtype=author&query=Balaprakash%2C+P">Prasanna Balaprakash</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Thomas Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Garcia-Cardona%2C+C">Cristina Garcia-Cardona</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Cohn%2C+J">Judith Cohn</a>, <a href="/search/q-bio?searchtype=author&query=Doroshow%2C+J">James Doroshow</a>, <a href="/search/q-bio?searchtype=author&query=Duan%2C+X">Xiaotian Duan</a>, <a href="/search/q-bio?searchtype=author&query=Dubinkina%2C+V">Veronika Dubinkina</a>, <a href="/search/q-bio?searchtype=author&query=Evrard%2C+Y">Yvonne Evrard</a>, <a href="/search/q-bio?searchtype=author&query=Fan%2C+Y+J">Ya Ju Fan</a>, <a href="/search/q-bio?searchtype=author&query=Gans%2C+J">Jason Gans</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+S">Stewart He</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+P">Pinyi Lu</a>, <a href="/search/q-bio?searchtype=author&query=Maslov%2C+S">Sergei Maslov</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Shukla%2C+M">Maulik Shukla</a>, <a href="/search/q-bio?searchtype=author&query=Stahlberg%2C+E">Eric Stahlberg</a>, <a href="/search/q-bio?searchtype=author&query=Wozniak%2C+J+M">Justin M. Wozniak</a>, <a href="/search/q-bio?searchtype=author&query=Yoo%2C+H">Hyunseung Yoo</a>, <a href="/search/q-bio?searchtype=author&query=Zaki%2C+G">George Zaki</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yitan Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2104.08961v2-abstract-short" style="display: inline;"> To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.08961v2-abstract-full').style.display = 'inline'; document.getElementById('2104.08961v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.08961v2-abstract-full" style="display: none;"> To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies, and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.08961v2-abstract-full').style.display = 'none'; document.getElementById('2104.08961v2-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 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 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">Accepted by Briefings in Bioinformatics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.02843">arXiv:2103.02843</a> <span> [<a href="https://arxiv.org/pdf/2103.02843">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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> <span class="tag is-small is-grey 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="Quantitative Methods">q-bio.QM</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/10.1098/rsfs.2021.0018">10.1098/rsfs.2021.0018 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bhati%2C+A+P">Agastya P. Bhati</a>, <a href="/search/q-bio?searchtype=author&query=Wan%2C+S">Shunzhou Wan</a>, <a href="/search/q-bio?searchtype=author&query=Alf%C3%A8%2C+D">Dario Alf猫</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A+R">Austin R. Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Bode%2C+M">Mathis Bode</a>, <a href="/search/q-bio?searchtype=author&query=Tan%2C+L">Li Tan</a>, <a href="/search/q-bio?searchtype=author&query=Titov%2C+M">Mikhail Titov</a>, <a href="/search/q-bio?searchtype=author&query=Merzky%2C+A">Andre Merzky</a>, <a href="/search/q-bio?searchtype=author&query=Turilli%2C+M">Matteo Turilli</a>, <a href="/search/q-bio?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/q-bio?searchtype=author&query=Highfield%2C+R+R">Roger R. Highfield</a>, <a href="/search/q-bio?searchtype=author&query=Rocchia%2C+W">Walter Rocchia</a>, <a href="/search/q-bio?searchtype=author&query=Scafuri%2C+N">Nicola Scafuri</a>, <a href="/search/q-bio?searchtype=author&query=Succi%2C+S">Sauro Succi</a>, <a href="/search/q-bio?searchtype=author&query=Kranzlm%C3%BCller%2C+D">Dieter Kranzlm眉ller</a>, <a href="/search/q-bio?searchtype=author&query=Mathias%2C+G">Gerald Mathias</a>, <a href="/search/q-bio?searchtype=author&query=Wifling%2C+D">David Wifling</a>, <a href="/search/q-bio?searchtype=author&query=Donon%2C+Y">Yann Donon</a>, <a href="/search/q-bio?searchtype=author&query=Di+Meglio%2C+A">Alberto Di Meglio</a>, <a href="/search/q-bio?searchtype=author&query=Vallecorsa%2C+S">Sofia Vallecorsa</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Heng Ma</a>, <a href="/search/q-bio?searchtype=author&query=Trifan%2C+A">Anda Trifan</a>, <a href="/search/q-bio?searchtype=author&query=Ramanathan%2C+A">Arvind Ramanathan</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Tom Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a> , et al. (4 additional authors not shown) </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="2103.02843v2-abstract-short" style="display: inline;"> The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.02843v2-abstract-full').style.display = 'inline'; document.getElementById('2103.02843v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.02843v2-abstract-full" style="display: none;"> The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.02843v2-abstract-full').style.display = 'none'; document.getElementById('2103.02843v2-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 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Interface Focus. 2021. 11 (6): 20210018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.12466">arXiv:2011.12466</a> <span> [<a href="https://arxiv.org/pdf/2011.12466">pdf</a>, <a href="https://arxiv.org/format/2011.12466">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> </div> </div> <p class="title is-5 mathjax"> Learning Curves for Drug Response Prediction in Cancer Cell Lines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Thomas Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Evrard%2C+Y+A">Yvonne A. Evrard</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yitan Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Yoo%2C+H">Hyunseung Yoo</a>, <a href="/search/q-bio?searchtype=author&query=Xia%2C+F">Fangfang Xia</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+S">Songhao Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Shukla%2C+M">Maulik Shukla</a>, <a href="/search/q-bio?searchtype=author&query=Fonstein%2C+M">Michael Fonstein</a>, <a href="/search/q-bio?searchtype=author&query=Doroshow%2C+J+H">James H. Doroshow</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2011.12466v1-abstract-short" style="display: inline;"> Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating data, a common question is whether the proposed predictors can further improve the generalization performance with more training data. We utilize empirical learning curves fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.12466v1-abstract-full').style.display = 'inline'; document.getElementById('2011.12466v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.12466v1-abstract-full" style="display: none;"> Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating data, a common question is whether the proposed predictors can further improve the generalization performance with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these predictors. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, suggesting that the shape of these curves depends on the unique model-dataset pair. The multi-input NN (mNN), in which gene expressions and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate predictors, providing a broader perspective on the overall data scaling characteristics. The fitted power law curves provide a forward-looking performance metric and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.12466v1-abstract-full').style.display = 'none'; document.getElementById('2011.12466v1-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> 24 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">14 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/2010.06574">arXiv:2010.06574</a> <span> [<a href="https://arxiv.org/pdf/2010.06574">pdf</a>, <a href="https://arxiv.org/format/2010.06574">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Saadi%2C+A+A">Aymen Al Saadi</a>, <a href="/search/q-bio?searchtype=author&query=Alfe%2C+D">Dario Alfe</a>, <a href="/search/q-bio?searchtype=author&query=Babuji%2C+Y">Yadu Babuji</a>, <a href="/search/q-bio?searchtype=author&query=Bhati%2C+A">Agastya Bhati</a>, <a href="/search/q-bio?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Thomas Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/q-bio?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/q-bio?searchtype=author&query=Coveney%2C+P">Peter Coveney</a>, <a href="/search/q-bio?searchtype=author&query=Trifan%2C+A">Anda Trifan</a>, <a href="/search/q-bio?searchtype=author&query=Brace%2C+A">Alex Brace</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/q-bio?searchtype=author&query=Gibbs%2C+T">Tom Gibbs</a>, <a href="/search/q-bio?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/q-bio?searchtype=author&query=Keipert%2C+K">Kristopher Keipert</a>, <a href="/search/q-bio?searchtype=author&query=Kurth%2C+T">Thorsten Kurth</a>, <a href="/search/q-bio?searchtype=author&query=Kranzlm%C3%BCller%2C+D">Dieter Kranzlm眉ller</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+H">Hyungro Lee</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Z">Zhuozhao Li</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Heng Ma</a>, <a href="/search/q-bio?searchtype=author&query=Merzky%2C+A">Andre Merzky</a>, <a href="/search/q-bio?searchtype=author&query=Mathias%2C+G">Gerald Mathias</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Yin%2C+J">Junqi Yin</a> , et al. (11 additional authors not shown) </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="2010.06574v1-abstract-short" style="display: inline;"> The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.06574v1-abstract-full').style.display = 'inline'; document.getElementById('2010.06574v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.06574v1-abstract-full" style="display: none;"> The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.06574v1-abstract-full').style.display = 'none'; document.getElementById('2010.06574v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.02431">arXiv:2006.02431</a> <span> [<a href="https://arxiv.org/pdf/2006.02431">pdf</a>, <a href="https://arxiv.org/format/2006.02431">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> <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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Babuji%2C+Y">Yadu Babuji</a>, <a href="/search/q-bio?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Tom Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/q-bio?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/q-bio?searchtype=author&query=Hong%2C+Z">Zhi Hong</a>, <a href="/search/q-bio?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Z">Zhuozhao Li</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xuefeng Liu</a>, <a href="/search/q-bio?searchtype=author&query=Ramanathan%2C+A">Arvind Ramanathan</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+Y">Yi Ren</a>, <a href="/search/q-bio?searchtype=author&query=Saint%2C+N">Nicholaus Saint</a>, <a href="/search/q-bio?searchtype=author&query=Schwarting%2C+M">Marcus Schwarting</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</a>, <a href="/search/q-bio?searchtype=author&query=van+Dam%2C+H">Hubertus van Dam</a>, <a href="/search/q-bio?searchtype=author&query=Wagner%2C+R">Rick Wagner</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="2006.02431v1-abstract-short" style="display: inline;"> Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.02431v1-abstract-full').style.display = 'inline'; document.getElementById('2006.02431v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.02431v1-abstract-full" style="display: none;"> Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.02431v1-abstract-full').style.display = 'none'; document.getElementById('2006.02431v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">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/2006.01171">arXiv:2006.01171</a> <span> [<a href="https://arxiv.org/pdf/2006.01171">pdf</a>, <a href="https://arxiv.org/format/2006.01171">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Duan%2C+X">Xiaotian Duan</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2006.01171v1-abstract-short" style="display: inline;"> We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.01171v1-abstract-full').style.display = 'inline'; document.getElementById('2006.01171v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.01171v1-abstract-full" style="display: none;"> We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of virtual screening: to detect as many of the top-performing treatments as possible. We outline history of virtual screening performance measures and the idea behind RES. We offer a python package and details on how to implement and interpret the results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.01171v1-abstract-full').style.display = 'none'; document.getElementById('2006.01171v1-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> 1 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.00095">arXiv:2005.00095</a> <span> [<a href="https://arxiv.org/pdf/2005.00095">pdf</a>, <a href="https://arxiv.org/format/2005.00095">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="Genomics">q-bio.GN</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"> A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Clyde%2C+A">Austin Clyde</a>, <a href="/search/q-bio?searchtype=author&query=Brettin%2C+T">Tom Brettin</a>, <a href="/search/q-bio?searchtype=author&query=Partin%2C+A">Alexander Partin</a>, <a href="/search/q-bio?searchtype=author&query=Shaulik%2C+M">Maulik Shaulik</a>, <a href="/search/q-bio?searchtype=author&query=Yoo%2C+H">Hyunseung Yoo</a>, <a href="/search/q-bio?searchtype=author&query=Evrard%2C+Y">Yvonne Evrard</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yitan Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Xia%2C+F">Fangfang Xia</a>, <a href="/search/q-bio?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2005.00095v2-abstract-short" style="display: inline;"> By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.00095v2-abstract-full').style.display = 'inline'; document.getElementById('2005.00095v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.00095v2-abstract-full" style="display: none;"> By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features. We found the inclusion of single nucleotide polymorphisms (SNPs) coded as count matrices improved model performance significantly, and no substantial difference in model performance with respect to molecular featurization between the common open source MOrdred descriptors and Dragon7 descriptors. Alongside this analysis, we outline data integration between CCL screening datasets and present evidence that new metrics and imbalanced data techniques, as well as advances in data standardization, need to be developed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.00095v2-abstract-full').style.display = 'none'; document.getElementById('2005.00095v2-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 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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> 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