Pharmacokinetic-Pharmacodynamic Trade-offs in SARS-CoV-2 Main Protease Inhibitors Unveiled through Machine Learning and Molecular Dynamics Simulations

The SARS-CoV-2 main protease (M _pro ) is a validated therapeutic target for inhibiting viral replication. Despite the screening of over 55,000 compounds, few candidates have advanced clinically, underscoring the difficulty in optimizing both target affinity and drug-like properties. Thus, developing effective M _pro inhibitors requires balancing high-affinity binding with favorable pharmacokinetic (PK) properties, such as solubility and permeability. To address this challenge, we integrated machine learning (ML) and molecular dynamics (MD) simulations to investigate the trade-offs between pharmacodynamic (PD) and PK properties in M _pro inhibitor design. We developed ML models to classify M _pro inhibitors based on experimental IC ₅₀ data, combining molecular descriptors with structural insights from MD simulations. Our Support Vector Machine (SVM) model achieved strong performance (training accuracy = 0.84, ROC AUC = 0.91; test accuracy = 0.79, ROC AUC = 0.86), while our logistic regression model (training accuracy = 0.78, ROC AUC = 0.85; test accuracy = 0.76, ROC AUC = 0.83) identified key molecular features influencing activity, including quantitative estimation of drug–likeness (QED), Log P and molecular weight (ExactMolWt). Notably, PK descriptors often exhibited opposing trends to binding affinity: hydrophilic features enhanced binding affinity but compromised PK properties, whereas hydrogen bonding, hydrophobic and π – π interactions in subsites S2 and S3/S4 are fundamental for binding affinity. Our findings highlight the need for a balanced approach in M _pro inhibitor design, strategically targeting these subsites may balance PD and PK properties. This study provides a computational framework for rational M _pro inhibitor discovery, combining ML and MD to investigate the complex interplay between enzyme inhibition and drug–likeness. These insights may guide in hit-to-lead optimization of the novel next-generation M _pro inhibitors of the SARS-CoV-2 with preclinical and clinical potential.