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Article overview
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Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation | | Han Huang
; Leilei Sun
; Bowen Du
; Weifeng Lv
; | | Date: |
1 Jan 2023 | | 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. | | Source: | arXiv, 2301.00427 | | Services: | Forum | Review | PDF | Favorites | |
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