High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation

Abstract

The dissociation or off rate, k_off, of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling molecular dynamics (MD) simulations that come at a high computational cost limiting their utility for drug design where a large number of ligands need to be evaluated. To overcome this, presented is a combined physics- and machine learning (ML)-based approach that uses the physics-based site identification by ligand competitive saturation (SILCS) method to enumerate potential ligand dissociation pathways and calculate ligand dissociation free-energy profiles along those pathways. The calculated free-energy profiles along with molecular properties are used as features to train ML models, including tree and neural network approaches, to predict k_off values. The protocol is developed and validated using 329 ligands for 13 proteins showing robustness of the ML workflow built upon the SILCS physics-based free-energy profiles. The resulting SILCS-Kinetics workflow offers a highly efficient method to study ligand dissociation kinetics, providing a powerful tool to facilitate drug design including the ability to generate quantitative estimates of atomic and functional groups contributions to ligand dissociation.