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 achieves competitive performance compared with recently reported state-of-the-art models in terms of generation diversity(COV-R, COV-P), accuracy (MAT-R, MAT-P), and property prediction for GEOM-QM9 and GEOM-Drugs datasets. In particular, on the GEOM-Drugs dataset, the average COV-R is improved by 3.75% compared with the best baseline model at a threshold (1.25 Å). Furthermore, a transfer model named GADIFF-NCI based on GADIFF is developed to generate conformations for noncovalent interaction (NCI) molecular systems. It takes GADIFF with GEOM-QM9 dataset as a pre-trained model, and incorporates a graph encoder for learning molecular vectors at the NCI molecular level. The resulting NCI molecular conformations are reasonable, as assessed by the evaluation of conformation and property predictions. This suggests that the proposed transferable model may hold noteworthy value for the study of multi-molecular conformations. The code and data of GADIFF is freely downloaded from https://github.com/WangDHg/GADIFF.

Keywords: diffusion generation model; graph neural network; molecular conformation generation; molecular property prediction; noncovalent interaction; transfer learning.