Computational Insights into Benzothiophene Derivatives as Potential Antibiotics Against Multidrug-Resistant Staphylococcus aureus: QSAR Modeling and Molecular Docking Studies

Abstract

Antimicrobial resistance (AMR) poses a critical global health threat, mainly due to multidrug-resistant Staphylococcus aureus (MRSA, MSSA, DRSA), which leads to significant morbidity and mortality. To address this issue, we conducted an investigation into benzothiophene derivatives as potential novel antibiotics using an integrative computational approach. Quantitative Structure-Activity Relationship (QSAR) models were developed via Partial Least Squares (PLS), Principal Component Regression (PCR), and Multiple Linear Regression (MLR), validated with external datasets (R² > 0.5). Principal Component Analysis (PCA) identified key descriptors (e.g., PEOE_VSA_FPOL, Q_VSA_FHYD), correlating molecular features with activity. QSAR models demonstrated strong predictive power (R² = 0.69–0.793, RMSE = 56.12–76.27). Molecular docking revealed critical interactions: compound 20 exhibited the highest binding affinity against MRSA (-6.38 kcal/mol), while 1 and 17 showed potent activity against MSSA (-5.56 kcal/mol) and DRSA (-5.23 kcal/mol), respectively. Notably, derivatives 18 and 26 displayed exceptional in vitro efficacy, with MIC values as low as 4 μg/mL against all strains. This research builds upon previous efforts by combining multi-model QSAR approaches with targeted docking, successfully identifying structurally optimized candidates. Our findings highlight the potential of benzothiophene derivatives as promising leads for combating multidrug-resistant infections, providing a roadmap for the rational design of new antibiotics.