用双图结构和3D扩散模型提升逆合成预测准确率
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
- 用分子图及其对偶图联合表示，增强反应中心识别
- 采用3D条件扩散模型生成完整反应物，提升合理性
- 适合药物分子设计与自动化合成路径规划的研究者
逆合成预测旨在识别能生成目标产物的反应物。传统方法分为反应中心识别和反应物生成两阶段。现有方法在两阶段均存在局限：(i) 未充分捕捉分子图中反应中心的“面”信息；(ii) 反应物生成多依赖2D序列生成，缺乏对完整反应基团分布的合理建模，且忽略分子的3D特性。为此，我们提出GDiffRetro。在反应中心识别中，创新性地融合原始分子图与其对偶图，引导模型关注图中的“面”结构；在反应物生成中，采用条件3D扩散模型，将得到的合成子转化为完整反应物。实验表明，GDiffRetro在多个评估指标上优于当前最先进的半模板模型。
原文摘要 · Abstract (English)
Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the ``face'' information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules' inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics.
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