E. Haura

Integrating molecular dynamics and machine learning to identify potential apo-state conformational and solvent-exposure signatures associated with resistant KRAS mutants

A computational framework integrating molecular dynamics (MD)-derived structural, energetic, thermodynamic, and contact-based descriptors with machine learning may inform the design of inhibitors targeting secondary KRAS resistance mutations, pending validation in additional structurally independent mutant systems.