High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models

• Published in: arXiv.org
• Main Authors: Stevenson, Garrett A, Jones, Derek, Kim, Hyojin, Drew Bennett, W F, Bennion, Brian J, Borucki, Monica, Bourguet, Feliza, Epstein, Aidan, Franco, Magdalena, Harmon, Brooke, He, Stewart, Katz, Max P, Kirshner, Daniel, Lao, Victoria, Lau, Edmond Y, Lo, Jacky, McLoughlin, Kevin, Mosesso, Richard, Murugesh, Deepa K, Negrete, Oscar A, Saada, Edwin A, Segelke, Brent, Maxwell, Stefan, Torres, Marisa W, Weilhammer, Dina, Wong, Sergio, Yang, Yue, Zemla, Adam, Zhang, Xiaohua, Zhu, Fangqiang, Lightstone, Felice C, Allen, Jonathan E
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 01.06.2021
• Subjects: Affinity; Binding; Coronaviruses; COVID-19; Inhibitors; Ligands; Machine learning; Model accuracy; Optimization; Proteins; Screening; Severe acute respiratory syndrome coronavirus 2; Viral diseases

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 fou...