Molecular Recognition of SARS-CoV-2 Mpro Inhibitors: Insights from Cheminformatics and Quantum Chemistry.

The SARS-CoV-2 main protease (Mpro), essential for viral replication, remains a prime target for antiviral drug design against COVID-19 and related coronaviruses. In this study, we present a systematic investigation into the molecular determinants of Mpro inhibition using an integrated approach combining large-scale data mining, cheminformatics, and quantum chemical calculations. A curated dataset comprising 963 high-resolution structures of Mpro-ligand complexes-348 covalent and 615 non-covalent inhibitors-was mined from the Protein Data Bank. Cheminformatics analysis revealed distinct physicochemical profiles for each inhibitor class: covalent inhibitors tend to exhibit higher hydrogen bonding capacity and sp character, while non-covalent inhibitors are enriched in aromatic rings and exhibit greater aromaticity and lipophilicity. A novel descriptor, Weighted Hydrogen Bond Count (WHBC), normalized for molecular size, revealed a notable inverse correlation with aromatic ring count, suggesting a compensatory relationship between hydrogen bonding and π-mediated interactions. To elucidate the energetic underpinnings of molecular recognition, 40 representative inhibitors (20 covalent, 20 non-covalent) were selected based on principal component analysis and aromatic ring content. Quantum mechanical calculations at the double-hybrid B2PLYP/def2-QZVP level quantified non-bonded interaction energies, revealing that covalent inhibitors derive binding strength primarily through hydrogen bonding (63.8%), whereas non-covalent inhibitors depend predominantly on π-π stacking and CH-π interactions (62.8%). Representative binding pocket analyses further substantiate these findings: the covalent inhibitor F2F-2020198-00X exhibited strong hydrogen bonds with residues such as Glu166 and His163, while the non-covalent inhibitor EDG-MED-10fcb19e-1 engaged in extensive π-mediated interactions with residues like His41, Met49, and Met165. The distinct interaction patterns led to the establishment of pharmacophore models, highlighting key recognition motifs for both covalent and non-covalent inhibitors. Our findings underscore the critical role of aromaticity and non-bonded π interactions in driving binding affinity, complementing or, in some cases, substituting for hydrogen bonding, and offer a robust framework for the rational design of next-generation Mpro inhibitors with improved selectivity and resistance profiles.