Markov chain Monte Carlo and nonequilibrium statistical mechanics in drug discovery
John Chodera,  The Chodera Lab, Memorial Sloan-Kettering Cancer Center

The rational design of small molecules for use as potential therapeutics or chemical probes for understanding biology is immensely difficult. Perhaps surprisingly, engineering novel small molecules that selectively bind biomolecular targets with high affinity, are soluble enough to reach useful physiological concentrations, and can permeate cells can all be expressed as ratios of normalizing constants (partition functions in statistical mechanics) that can be computed using Markov chain Monte Carlo techniques. We will give an overview of recent developments in efficient MCMC methods that borrow from both statistical inference and nonequilibrium statistical mechanics to greatly accelerate the rate at which these quantities can be computed, and describe new algorithms that have the potential to automatically and efficiently guide the design of novel small molecules.