Learning a force field from small-molecule crystal lattice predictions enables consistent sub-Angstrom protein-ligand docking

Abstract Accurate and rapid calculation of protein-small molecule interaction energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here we describe a new approach in which small-molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1,386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized so native crystal lattice arrangements had lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1,112 complexes was improved by more than 10% over previously published methods, with solutions within <1 ? in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for systematically improving computational drug discovery.