Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
This addresses ligand-based drug design by enabling the generation of drug candidates with specific 3D shapes, which is incremental as it builds on existing diffusion models.
The paper tackled the problem of generating 3D molecule structures conditioned on molecular shapes for drug design, resulting in a model that produces novel, diverse, drug-like molecules with similar shapes to the input.
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.