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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/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> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <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> 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