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

TL;DR

This study demonstrates a rapid, structure-based computational approach combining molecular dynamics, free energy calculations, adaptive sampling, and machine learning to discover and validate potent SARS-CoV-2 Mpro inhibitors, progressing from initial non-covalent to covalent compounds with sub-micromolar activity.

Contribution

It introduces an integrated computational-experimental pipeline for SARS-CoV-2 Mpro inhibitor discovery, emphasizing the use of high-performance simulations and structure-based design for drug optimization.

Findings

Identified inhibitors with IC50 as low as 830 nM.

Validated computational predictions with NMR and in vitro assays.

Proposed fluorinated tetrahydroquinolines as promising lead compounds.

Abstract

We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (M$^{pro}$) inhibitors whose potency range from mM for initial non-covalent ligands to sub-$\mu$M 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 ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces. Machine Learning predictions are also performed to predict selected compound properties. While simulations extensively use High Performance Computing to strongly reduce time-to-solution, they were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and to in vitro characterization of compounds. Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem. The proposed fluorinated tetrahydroquinolines open routes for further optimization of M$^{pro}$ inhibitors towards low nM affinities.