用离散流模型模拟化学反应中电子的跳跃式变化，实现正向与逆向反应预测。
SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction
- 基于图结构的离散流模型，捕捉原子和键的离散状态变化。
- 在三个基准数据集上达到最先进性能，正向与逆合成任务均表现优异。
- 适合药物分子设计、反应路径预测等需要双向生成的化学研究者。
化学反应的本质在于电子的重新分布与重组，常表现为电子转移或电子对迁移。这些变化在物理世界中本质上是离散且突变的，如原子电荷状态的改变或化学键的形成与断裂。为建模状态转换，我们提出SynBridge，一种用于多任务反应预测的双向流生成模型。通过图到图的Transformer网络架构及任意两个离散分布间的离散流桥接，SynBridge利用原子与键的离散状态，捕获反应物与产物间的双向化学转化。我们在三个基准数据集（USPTO-50K、USPTO-MIT、Pistachio）上进行大量实验，验证了方法的有效性，在正向反应与逆合成任务中均达到当前最优性能。消融实验与噪声调度分析表明，结构化扩散在离散空间中的优势显著。
原文摘要 · Abstract (English)
The essence of a chemical reaction lies in the redistribution and reorganization of electrons, which is often manifested through electron transfer or the migration of electron pairs. These changes are inherently discrete and abrupt in the physical world, such as alterations in the charge states of atoms or the formation and breaking of chemical bonds. To model the transition of states, we propose SynBridge, a bidirectional flow-based generative model to achieve multi-task reaction prediction. By leveraging a graph-to-graph transformer network architecture and discrete flow bridges between any two discrete distributions, SynBridge captures bidirectional chemical transformations between graphs of reactants and products through the bonds' and atoms' discrete states. We further demonstrate the effectiveness of our method through extensive experiments on three benchmark datasets (USPTO-50K, USPTO-MIT, Pistachio), achieving state-of-the-art performance in both forward and retrosynthesis tasks. Our ablation studies and noise scheduling analysis reveal the benefits of structured diffusion over discrete spaces for reaction prediction.
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