  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. We here present a
graph-based generative modeling technology that encodes explicit 3D
protein-ligand contacts within a relational graph architecture. 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 binding
pocket of the dopamine D2 receptor 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 among highest-ranked docking poses with a
high recovery rate. This work shows how the structural context of a protein
target can be used to enhance molecule generation.
