We perform quantum mechanics-based ranking refinement and binding analysis of the toplisted SARS-CoV-2 spike (S) protein inhibitor candidates as identified in classical docking simulations [1]. As computational “workhorse” we employ the linear scaling fragment molecular orbital density-functional tight-binding (FMO-DFTB) method [2] including solvent effects via the polarizable continuum model (PCM) [3]. Focusing on the reported list of top-1000 potential candidates and their binding poses with various protein conformations, we perform FMO-DFTB/PCM geometry optimizations of ligands and partial geometry optimizations of the protein and ligand-protein complexes. The pair interaction energies (PIEs) between the ligands and each residue of the protein, including solvent free energy effects, will are analyzed for all optimized complexes. The re-ranked top-100 binding complexes are validated and further refined using FMO-MP2/PCM single point energy calculations. Our contribution serves to short-list the number of candidate compounds predicted by the classical docking simulations for submission to experimental tests. The pair interaction energy (PIE) analysis is hoped to inform future, de-novo inhibitor drug design.

Quantum chemistry-based refinement of SARS-CoV-2 inhibitors from classical docking

ACS Fall 2021

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