  Ligand-based drug design aims to identify novel drug candidates of similar
shapes with known active molecules. In this paper, we formulated an in silico
shape-conditioned molecule generation problem to generate 3D molecule
structures conditioned on the shape of a given molecule. To address this
problem, we developed a translation- and rotation-equivariant shape-guided
generative model ShapeMol. ShapeMol consists of an equivariant shape encoder
that maps molecular surface shapes into latent embeddings, and an equivariant
diffusion model that generates 3D molecules based on these embeddings.
Experimental results show that ShapeMol can generate novel, diverse, drug-like
molecules that retain 3D molecular shapes similar to the given shape condition.
These results demonstrate the potential of ShapeMol in designing drug
candidates of desired 3D shapes binding to protein target pockets.
