High-Throughput virtual screening of small molecule inhibitors for sars-cov-2 protein targets with deep fusion models

Garrett A. Stevenson, Derek Jones, Hyojin Kim, W. F.Drew Bennett, Brian Bennion, Monica Borucki, Feliza Bourguet, Aidan Epstein, Magdalena Franco, Brooke Harmon, Stewart He, Max P. Katz, Daniel Kirshner, Victoria Lao, Edmond Y. Lau, Jacky Lo, Kevin McLoughlin, Richard Mosesso, Deepa K. Murugesh, Oscar A. NegreteEdwin A. Saada, Brent Segelke, Maxwell Stefan, Marisa W. Torres, Dina Weilhammer, Sergio Wong, Yue Yang, Adam Zemla, Xiaohua Zhang, Fangqiang Zhu, Felice C. Lightstone, Jonathan E. Allen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of SC 2021
Subtitle of host publicationThe International Conference for High Performance Computing, Networking, Storage and Analysis: Science and Beyond
PublisherIEEE Computer Society
ISBN (Electronic)9781450384421
DOIs
StatePublished - Nov 14 2021
Externally publishedYes
Event33rd International Conference for High Performance Computing, Networking, Storage and Analysis: Science and Beyond, SC 2021 - Virtual, Online, United States
Duration: Nov 14 2021Nov 19 2021

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference33rd International Conference for High Performance Computing, Networking, Storage and Analysis: Science and Beyond, SC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period11/14/2111/19/21

Keywords

  • AI
  • COVID-19
  • deep learning
  • GPU
  • HPC
  • Hyper-parameter optimization
  • SARS-CoV-2

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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