Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics

The determination of drug residence times, which define the time an inhibitor is in complex with itstarget, is a fundamental part of the drug discovery process. Synthesis and experimentalmeasurements of kinetic rate constants are, however, expensive, and time-consuming. In this work,we aimed to obtain drug residence times computationally. Furthermore, we propose a novelalgorithm to identify molecular design objectives based on ligand unbinding kinetics. We designedan enhanced sampling technique to accurately predict the free energy profiles of the ligandunbinding process, focusing on the free energy barrier for unbinding. Our method first identifiesunbinding paths determining a corresponding set of internal coordinates (IC) that form contactsbetween the protein and the ligand, it then iteratively updates these interactions during a series ofbiased molecular-dynamics (MD) simulations to reveal the ICs that are important for the whole ofthe unbinding process. Subsequently, we performed finite temperature string simulations to obtainthe free energy barrier for unbinding using the set of ICs as a complex reaction coordinate.Importantly, we also aimed to enable further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs fromunbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the stringunbinding path. We demonstrate that our ML method can identify key ligand-protein interactionsdriving the system through the TS. Some of the most important drugs for cancer treatment arekinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealingtarget for anticancer drug development. Here, we tested our method using two different CDK2inhibitors for potential further development of these compounds. We compared the free energybarriers obtained from our calculations with those observed in available experimental data. Wehighlighted important interactions at the distal ends of the ligands that can be targeted forimproved residence times. Our method provides a new tool to determine unbinding rates, and toidentify key structural features of the inhibitors that can be used as starting points for novel designstrategies in drug discovery.