SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction
This work addresses the challenge of bidirectional reaction prediction for chemists and drug discovery, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of predicting chemical reactions in both forward and retrosynthesis directions by modeling discrete state transitions, achieving state-of-the-art performance on benchmark datasets like USPTO-50K, USPTO-MIT, and Pistachio.
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.