Accelerating fragment-based drug discovery using grand canonical nonequilibrium candidate Monte Carlo

Abstract

Abstract Fragment-based drug discovery is a popular approach in the early stages of drug development. Computational tools are integral to these campaigns, providing a route to library design, virtual screening, the identification of putative small-molecule binding sites, the elucidation of binding geometries, and the prediction of accurate binding affinities. In this context, molecular dynamics-based simulations are increasingly popular, but often limited by sampling issues. Here, we develop grand canonical nonequilibrium candidate Monte Carlo (GCNCMC) to overcome these limitations. GCNCMC attempts the insertion and deletion of fragments to, or from, a region of interest; each proposed move is subject to a rigorous acceptance test based on the thermodynamic properties of the system. We demonstrate that fragment-based GCNCMC efficiently finds occluded fragment binding sites and accurately samples multiple binding modes. Finally, binding affinities of fragments are successfully calculated without the need for restraints, the handling of multiple binding modes, or symmetry corrections.