RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Yiming Wang Yuxuan Song Minkai Xu Rui Wang Hao Zhou Weiying Ma
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
groups from the dummy distribution given products and then generate the
external bonds to connect the products and generated groups. 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.