High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models

@article{Stevenson2021HighthroughputVS,
  title={High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models},
  author={Garrett Stevenson and Derek Jones and Hyojin Kim and W. F. Drew Bennett and Brian J. Bennion and Monica K. Borucki and Feliza A. Bourguet and Aidan T Epstein and Magdalena Franco and Brooke Harmon and Stewart He and Max P. Katz and Daniel A. Kirshner and Victoria Lao and Edmond Y. Lau and Jacky Kai-Yin Lo and Kevin S. McLoughlin and Richard A Mosesso and Deepa K. Murugesh and Oscar A. Negrete and Edwin A. Saada and Brent W. Segelke and Maxwell A. Stefan and Marisa W. Torres and Dina R. Weilhammer and Sergio Ernesto Wong and Yue Yang and Adam T. Zemla and Xiaohua Zhang and Fangqiang Zhu and Felice C. Lightstone and Jonathan E. Allen},
  journal={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  year={2021}
}
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… 

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