Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

Zhe Li, Chengkun Wu, Yishui Li, Runduo Liu, Kai Lu, Ruibo Wang, Jie Liu, Chunye Gong, Canqun Yang, Xin Wang, Chang Guo Zhan, Hai Bin Luo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 500,000 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.

Original languageEnglish
Pages (from-to)45-57
Number of pages13
JournalInternational Journal of High Performance Computing Applications
Issue number1
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.


  • SARS-CoV-2
  • Supercomputing
  • absolute binding free energy
  • free energy perturbation
  • virtual screening

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture


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