@inproceedings{9003cd90b87a44d4b7bd7425683f13bd,
title = "High-Throughput virtual screening of small molecule inhibitors for sars-cov-2 protein targets with deep fusion models",
abstract = "Structure-based Deep Fusion modelswere recently shown to outperform several physicsand machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve bindingaffinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-Throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-Throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.",
keywords = "AI, COVID-19, deep learning, GPU, HPC, Hyper-parameter optimization, SARS-CoV-2",
author = "Stevenson, {Garrett A.} and Derek Jones and Hyojin Kim and Bennett, {W. F.Drew} and Brian Bennion and Monica Borucki and Feliza Bourguet and Aidan Epstein and Magdalena Franco and Brooke Harmon and Stewart He and Katz, {Max P.} and Daniel Kirshner and Victoria Lao and Lau, {Edmond Y.} and Jacky Lo and Kevin McLoughlin and Richard Mosesso and Murugesh, {Deepa K.} and Negrete, {Oscar A.} and Saada, {Edwin A.} and Brent Segelke and Maxwell Stefan and Torres, {Marisa W.} and Dina Weilhammer and Sergio Wong and Yue Yang and Adam Zemla and Xiaohua Zhang and Fangqiang Zhu and Lightstone, {Felice C.} and Allen, {Jonathan E.}",
note = "Funding Information: The authors gratefully acknowledge Professor Xinquan Wang (Ts-inghua University) for providing early access to his crystal structure of the ACE2-RBD complex. The authors gratefully acknowledge extensive computer time provided by Livermore Computing. Part of this research was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act. The authors thank Lawrence Livermore National Laboratory for funding Laboratory Directed Research and Development projects 20-ERD-065 and 20-ERD-062. Part of this research was also supported by the American Heart Association under CRADA TC02274-4 and the National Nuclear Security Administration through the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium under CRADA TC02349 . This work was funded in part by DTRA under award HDTRA1036045. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy{\textquoteright}s National Nuclear Security Administration under contract DE-NA0 0 03525. All work performed at Lawrence Livermore National Laboratory is performed under the auspices of the U.S. Department of Energy under Contract DE-AC52-07NA27344. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: {\textcopyright} 2021 IEEE Computer Society. All rights reserved.; 33rd International Conference for High Performance Computing, Networking, Storage and Analysis: Science and Beyond, SC 2021 ; Conference date: 14-11-2021 Through 19-11-2021",
year = "2021",
month = nov,
day = "14",
doi = "10.1145/3458817.3476193",
language = "English (US)",
series = "International Conference for High Performance Computing, Networking, Storage and Analysis, SC",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of SC 2021",
}