Abstract
Predicting the kinetics of drug-protein interactions is crucial for understanding drug efficacy, particularly in personalized medicine, where protein mutations can significantly alter drug residence times. This study applies replica exchange transition interface sampling and its partial path variant to investigate the dissociation kinetics of imatinib from Abelson nonreceptor tyrosine kinase (ABL) and mutants relevant to chronic myeloid leukemia therapy. These path sampling methods offer a bias-free alternative to conventional approaches requiring qualitative predefined reaction coordinates. Nevertheless, the complex free energy landscape of ABL-imatinib dissociation presents significant challenges. Multiple metastable states and orthogonal barriers lead to parallel unbinding pathways, complicating convergence in transition interface sampling-based methods. Despite employing computational efficiency strategies such as asynchronous replica exchange, full convergence remained elusive. This work provides a critical assessment of path sampling in high-dimensional biological systems, discussing the need for enhanced initialization strategies, advanced Monte Carlo path generation moves, and machine learning-derived reaction coordinates to improve kinetic predictions of drug dissociation with minimal prior knowledge.