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

Abstract The SARS-CoV-2 main protease (Mpro) 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 Mpro 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 Mpro inhibitor design. We developed ML models to classify Mpro inhibitors based on experimental IC50 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 Mpro inhibitor design, strategically targeting these subsites may balance PD and PK properties. This study provides a computational framework for rational Mpro 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 Mpro inhibitors of the SARS-CoV-2 with preclinical and clinical potential. Competing Interest Statement The authors have declared no competing interest.