High-Throughput Virtual Screening of Small Molecule Inhibitors for
SARS-CoV-2 Protein Targets with Deep Fusion Models
- URL: http://arxiv.org/abs/2104.04547v1
- Date: Fri, 9 Apr 2021 18:18:26 GMT
- Title: High-Throughput Virtual Screening of Small Molecule Inhibitors for
SARS-CoV-2 Protein Targets with Deep Fusion Models
- Authors: Garrett A. Stevenson, Derek Jones, Hyojin Kim, W. F. Drew Bennett,
Brian J. 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. Negrete, Edwin 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
- Abstract summary: Over 500 million small molecules were 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.
- Score: 3.8075853084146023
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Structure-based Deep Fusion models were recently shown to outperform several
physics- and 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 binding-affinity 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.
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