Benchmarking the Ability of Common Docking Programs to Correctly Reproduce and Score Binding Modes in SARS-CoV-2 Protease Mpro

Shani Zev, Keren Raz, Renana Schwartz, Reem Tarabeh, Prashant Kumar Gupta, Dan T. Major

Research output: Contribution to journalArticlepeer-review


The coronavirus SARS-CoV-2 main protease, Mpro, is conserved among coronaviruses with no human homolog and has therefore attracted significant attention as an enzyme drug target for COVID-19. The number of studies targeting Mpro for in silico screening has grown rapidly, and it would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. Clearly, current attempts at designing drugs targeting Mpro with the aid of computational docking would benefit from a priori knowledge of the ability of docking programs to predict correct binding modes and score these correctly. In the current work, we tested the ability of several leading docking programs, namely, Glide, DOCK, AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of Mpro ligands in 193 crystal structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of Mpro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction with virtual screening and docking.

Original languageEnglish
Pages (from-to)2957-2966
Number of pages10
JournalJournal of Chemical Information and Modeling
Issue number6
StatePublished - 28 Jun 2021

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Chemistry(all)
  • Library and Information Sciences
  • Computer Science Applications


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