Proteins in complex with small-molecule ligands represent the core of structure-based drug discovery. However, three-dimensional representations are absent from most deep-learning-based generative models. Here, we present a graph-based generative modeling technology that encodes explicit 3D protein-ligand contacts within a relational graph architecture and evaluate its behavior using the dopamine D2 receptor (DD2R) as a model system. The models combine a conditional variational autoencoder that allows for activity-specific molecule generation with putative contact generation that provides predictions of molecular interactions within the target-binding pocket. We show that molecules generated with our 3D procedure are more compatible with the DD2R-binding pocket than those produced by a comparable ligand-based 2D generative method, as measured by docking scores, expected stereochemistry, and recoverability in commercial chemical databases. Predicted protein-ligand contacts were found to be among the highest-ranked docking poses with a high recovery rate. Overall, this work shows how the structural context of a protein target can enhance the generation of small molecules within a realistic binding environment.
Kang et al. (Wed,) studied this question.