Abstract: Rational drug design depends on the ability to predict both the three-dimensional structures of candidate molecules bound to their targets and the associated binding affinities. Such predictions are generally informed by either the target’s 3D structure or binding affinity measurements for other molecules at the target. We developed a rigorous statistical framework to combine these two sources of information. Our framework allows nonstructural data—a list of ligands that are known to bind the same target but for which no 3D structure is available—to be used to improve binding pose predictions and improves virtual screening enrichments as compared to simple combinations of physics-based docking and ligand-based modeling.
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