RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation

Yiming Wang, Yuxuan Song, Minkai Xu, Rui Wang, Hao Zhou, Wei-Ying Ma

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Keywords: Retrosynthesis, Diffusion Model

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Abstract:

Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task.
Inspired by the recent advancements in discrete diffusion models for graph generation, we introduce RetroSynthesis Diffusion (RetroDiff), a novel diffusion-based method designed to address this problem.
However, integrating a diffusion-based graph-to-graph framework while retaining essential chemical reaction template information presents a notable challenge.
Our key innovation is to develop a multi-stage diffusion process. In this method, we decompose the retrosynthesis procedure to first sample external graph motifs from the dummy distribution given products and then generate the external bonds to connect the products and generated motifs. Interestingly, such a generation process is exactly the reverse of the widely adapted semi-template retrosynthesis procedure, i.e. from reaction center identification to synthon completion, which significantly reduces the error accumulation.
Experimental results on the benchmark have demonstrated the superiority of our method over all other semi-template methods.

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Submission Number: 4613