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Diffusion-based Molecule Generation with Informative Prior Bridges
Chengyue Gong · Lemeng Wu · Xingchao Liu · Mao Ye · Qiang Liu
Event URL: https://openreview.net/forum?id=QagNEt9k8Vi »

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for high-quality molecule generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores.

Author Information

Chengyue Gong (University of Texas at Austin)
Lemeng Wu (The University of Texas at Austin)
Xingchao Liu (University of Texas, Austin)
Mao Ye (The University of Texas at Austin)
Qiang Liu (Dartmouth College)

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