  Shape-based virtual screening is widely employed in ligand-based drug design
to search chemical libraries for molecules with similar 3D shapes yet novel 2D
chemical structures compared to known ligands. 3D deep generative models have
the potential to automate this exploration of shape-conditioned 3D chemical
space; however, no existing models can reliably generate valid drug-like
molecules in conformations that adopt a specific shape such as a known binding
pose. We introduce a new multimodal 3D generative model that enables
shape-conditioned 3D molecular design by equivariantly encoding molecular shape
and variationally encoding chemical identity. We ensure local geometric and
chemical validity of generated molecules by using autoregressive fragment-based
generation with heuristic bonding geometries, allowing the model to prioritize
the scoring of rotatable bonds to best align the growing conformational
structure to the target shape. We evaluate our 3D generative model in tasks
relevant to drug design including shape-conditioned generation of chemically
diverse molecular structures and shape-constrained molecular property
optimization, demonstrating its utility over virtual screening of enumerated
libraries.
