Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19. (January 2023)

• Record Type: Journal Article
• Authors: Li, Zhe; Wu, Chengkun; Li, Yishui; Liu, Runduo; Lu, Kai; Wang, Ruibo; Liu, Jie; Gong, Chunye; Yang, Canqun; Wang, Xin; Zhan, Chang-Guo; Luo, Hai-Bin
• Other Names: Parsons Mark guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 37:Number 1(2023)
• Page Start: 45
• Page End: 57
• Publication Date: 2023-01
• Subjects: Supercomputing -- SARS-CoV-2 -- free energy perturbation -- absolute binding free energy -- virtual screening; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/10943420221117797
• Languages: English
• ISSNs: 1094-3420

Abstract

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 M pro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12, 000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500, 000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards M pro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.