Geometry-Complete Diffusion for 3D Molecule Generation and Optimization.

ABSTRACT:

Motivation

Denoising diffusion probabilistic models (DDPMs) have recently taken the field of generative modeling by storm, pioneering new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along this latter line of research, methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a DDPM framework. However, such methods are unable to learn important geometric and physical properties of 3D molecules during molecular graph generation, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which negatively impacts their ability to effectively scale to dat

SUBMITTER: Morehead A 

PROVIDER: S-EPMC9934735 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature