Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

SARS-CoV-2 3C-like protease (3CLpro) is an essential viral enzyme responsible for processing viral polyproteins and is a validated target for small-molecule therapeutic intervention. This study presents an integrative cheminformatics and systems biology framework to identify and evaluate potential 3CLpro inhibitors. A curated dataset of 919 compounds from the CHEMBL database was used to develop predictive QSAR models based on substructure fingerprints and 1D/2D molecular descriptors. The best-performing models achieved strong correlation coefficients (0.9736 for training and 0.7413 for testing), and key molecular features were identified using feature importance analysis. A web-based tool, 3CLpro-Pred was developed to enable rapid prediction of compound bioactivity. Molecular docking and dynamics simulations further validated QSAR findings by elucidating key atomic-level interactions at the protease active site. Top hit compounds were prioritized for systems-level analysis at the host-pathogen interface, where gene ontology and KEGG pathway enrichment revealed their potential to modulate key host signalling pathways. Critical regulatory genes, including <i>TBK1, PIK3CA, IKBKB, GSK3B</i>, and <i>CASP3</i>, were identified as major nodes linking compound action to antiviral and immune processes. This study delivers a combinatorial computational pipeline to accelerate the discovery of 3CLpro-targeted antivirals, providing mechanistic insights and a shortlist of candidates for future experimental validation.