Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

Léa El Khoury, Zhifeng Jing, Alberto Cuzzolin, Alessandro Deplano, Daniele Loco, Boris Sattarov, Florent Hédin, Sebastian Wendeborn, Chris Ho, Dina El Ahdab, Theo Jaffrelot Inizan, Mattia Sturlese, Alice Sosic, Martina Volpiana, Angela Lugato, Marco Barone, Barbara Gatto, Maria Ludovica Macchia, Massimo Bellanda, Roberto BattistuttaCristiano Salata, Ivan Kondratov, Rustam Iminov, Andrii Khairulin, Yaroslav Mykhalonok, Anton Pochepko, Volodymyr Chashka-Ratushnyi, Iaroslava Kos, Stefano Moro, Matthieu Montes, Pengyu Ren, Jay W. Ponder, Louis Lagardère, Jean Philip Piquemal, Davide Sabbadin

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand-protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.

Original languageEnglish
Pages (from-to)3674-3687
Number of pages14
JournalChemical Science
Issue number13
StatePublished - Feb 10 2022


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