GADIFF: a transferable graph attention diffusion model for generating molecular conformations.

ABSTRACT: The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features. In addition, we utilize MSA to calculate dynamic noise weights to boost molecular conformation noise prediction. Upon the improvements, GADIFF

SUBMITTER: Wang D 

PROVIDER: S-EPMC11684900 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature