Modelling Chemical Reasoning to Predict Reactions
This work addresses the challenge of reaction prediction for organic chemists, enabling high-throughput hypothesis generation for reaction discovery, though it is incremental as it builds on existing knowledge graph methods.
The authors tackled the problem of predicting chemical reactions by modeling chemical reasoning as finding missing links in a knowledge graph, resulting in a model that outperforms a rule-based expert system on 180,000 reactions and generalizes beyond known reaction types.
The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically achieved in a sub-second time frame, our model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.