Computational Free Energy Approaches to Trimethoprim Resistance: Progress, Challenges, and Clinical Relevance

Abstract

The rapid emergence of antimicrobial resistance (AMR) has necessitated the development of predictive tools capable of identifying drug-resistant mutations in clinically relevant timeframes. Trimethoprim, a widely prescribed antibiotic targeting bacterial dihydrofolate reductase (DHFR), has been compromised by the accumulation of resistance-conferring point mutations. Conventional phenotypic assays and sequence-based diagnostics often fail to capture the functional impact of novel or rare variants. In recent years, alchemical free energy methods rigorous, physics-based computational approaches rooted in statistical mechanics have emerged as promising tools for predicting the effect of mutations on drug binding affinity. This review critically examines the theoretical foundations, methodological advancements, validation studies, and clinical translational potential of alchemical free energy calculations for classifying trimethoprim-resistance mutations. We discuss challenges related to accuracy, computational cost, and integration into clinical workflows, and highlight emerging strategies that may enable routine deployment of these methods in precision antimicrobial therapy.