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

Computational Benzothiophene Derivatives : QSAR Modeling and Molecular Docking Studies

Abstract

This study investigates the potential of benzothiophene derivatives as novel antibiotics against multidrug-resistant strains of Staphylococcus aureus. We employ Quantitative Structure-Activity Relationship (QSAR) modeling, using Principal Component Analysis (PCA) for descriptor selection. Our models are developed using Partial Least Squares (PLS), Principal Component Regression (PCR), and Multiple Linear Regression (MLR). The models exhibit strong predictive capabilities, which are validated with external datasets. We identify key descriptors that establish significant correlations between molecular structures and antimicrobial activity. Additionally, molecular docking studies reveal critical interactions between the target proteins and the compounds. Compounds 20, 1 and 17 notably demonstrate high binding affinities against MRSA, MSSA, and daptomycin-resistant strains. This integrative computational approach underscores the potential of these derivatives as promising candidates for addressing antibiotic resistance.