FEP-Based Large-Scale Virtual Screening for Effective Drug Discovery against COVID-19

Authors: Zhe Li (Sun Yat-sen University, Guangzhou, China); Chengkun Wu and Yishui Li (State Key Laboratory of High Performance Computing, Changsha); Runduo Liu (Sun Yat-sen University, Guangzhou, China); Kai Lu, Ruibo Wang, Jie Liu, and Chunye Gong (State Key Laboratory of High Performance Computing, Changsha); Canqun Yang (National Supercomputing Center, Tianjin); Xin Wang (Ocean University of China, Qingdao); Chang-Guo Zhan (University of Kentucky); and Hai-Bin Luo (Sun Yat-sen University, Guangzhou, China)

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 Mpro 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 0.5 million MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, 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.




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