Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

Autor:	Huang, Han, Sun, Leilei, Du, Bowen, Lv, Weifeng
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Quantitative Biology - Biomolecules
Druh dokumentu:	Working Paper
Popis:	Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps. Our code is provided in https://github.com/GRAPH-0/CDGS. Comment: Accepted by AAAI 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.00427 Zobrazit plný text záznamu View this record from Arxiv