  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.
