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Deep generative models have been applied with increasing success to thegeneration of two dimensional molecules as SMILES strings and molecular graphs.In this work we describe for the first time a deep generative model that cangenerate 3D molecular structures conditioned on a three-dimensional (3D)binding pocket. Using convolutional neural networks, we encode atomic densitygrids into separate receptor and ligand latent spaces. The ligand latent spaceis variational to support sampling of new molecules. A decoder networkgenerates atomic densities of novel ligands conditioned on the receptor.Discrete atoms are then fit to these continuous densities to create molecularstructures. We show that valid and unique molecules can be readily sampled fromthe variational latent space defined by a reference `seed' structure andgenerated structures have reasonable interactions with the binding site. Asstructures are sampled farther in latent space from the seed structure, thenovelty of the generated structures increases, but the predicted bindingaffinity decreases. Overall, we demonstrate the feasibility of conditional 3Dmolecular structure generation and provide a starting point for methods thatalso explicitly optimize for desired molecular properties, such as high bindingaffinity.

Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models