Leveraging non-structural data to predict structures of protein–ligand complexes

Structure-based drug design depends on the ability to predict the three-dimensional structure of ligands bound to their targets, as does understanding the molecular mechanisms of many essential biological processes. Dozens of computational docking methods have been developed to address this binding pose prediction problem, but they frequently produce inaccurate results. Here we present a method that substantially improves the accuracy of binding pose prediction by exploiting a widely available source of non-structural information: a list of other ligands that bind the same target. Our method, ComBind, quantifies and leverages the chemist’s intuition that even very different ligands tend to form similar interactions with a target protein. We demonstrate that ComBind consistently increases pose prediction accuracy across all major families of drug targets. We also illustrate its use by predicting previously unknown binding poses of antipsychotics and validating these results experimentally.