UC Irvine

Early stage drug discovery would change dramatically if computational methods could accurately and quickly predict binding modes and affinities of compounds in advance of experiments. Simulation based approaches like classical molecular dynamics (MD) simulations have gained traction as a useful tool for early stage drug discovery, as MD provides a full atomistic and dynamic view of the biological system of interest. In principle, MD simulations can be a powerful tool for lead optimization as MD can provide knowledge of the ligand's binding mode, dynamics, and even binding affinity. In order to accurately compute binding affinities or predict ligand binding modes, simulations must run long enough to capture the relevant biological event or sufficiently sample all the physically relevant conformations. My research presents the development of new simulation approaches for accelerated sampling and demonstrates the use of non-equilibrium candidate Monte Carlo moves with MD simulations for predicting ligand binding modes to a pharmaceutically relevant target.