Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

• URL: http://arxiv.org/abs/2301.00427v2
• Date: Tue, 23 May 2023 09:41:57 GMT
• Title: Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
• Authors: Han Huang, Leilei Sun, Bowen Du, Weifeng Lv
• Abstract summary: We propose a 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 differential equations (SDE) We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states.
• Score: 32.66694406638287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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