@inproceedings{
chen2023shapeconditioned,
title={Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models},
author={Ziqi Chen and Bo Peng and srinivasan parthasarathy and Xia Ning},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=JWfvMT43pZ}
}
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 an equivariant shape-conditioned generative model $\mathsf{ShapeMol}$. $\mathsf{ShapeMol}$ 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 $\mathsf{ShapeMol}$ can generate novel, diverse, drug-like molecules that retain 3D molecular shapes similar to the given shape condition. These results demonstrate the potential of $\mathsf{ShapeMol}$ in designing drug candidates of desired 3D shapes binding to
protein target pockets.
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ziqi Chen Bo Peng srinivasan parthasarathy Xia Ning