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

2021 
We report the fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM 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 used to rationalize different ligands binding poses through the explicit reconstruction of the ligand-protein conformational space. Machine learning predictions are also utilized to predict selected compound properties. Computations were performed on GPU-accelerated supercomputers and high-performance cloud infrastructures to exponentially reduce time-to-solution, and were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and in vitro characterization of compounds. The study highlights the power of in silico strategies that rely on structure-based approaches for drug design and address protein conformational heterogeneity. The proposed scaffolds open a path toward further optimization of Mpro inhibitors with nM affinities.
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