Learning Beyond the Endpoints: Benchmarking and Learning Transition-State-Informed Reaction Representations

Representation learning is central to machine learning for chemistry, yet learning from reactant and product structures alone leaves information about the intervening transformation underexplored. We investigate how much transition-state (TS) information benefits reaction representations and how these benefits can be transferred to predictors that use only reactants and products at deployment. We introduce Transition-State-Informed Representation Learning (TSIRL), a benchmark and learning framework that evaluates the value of direct TS access and its transfer to endpoint-only prediction. The benchmark comprises four audited reaction datasets with aligned reactant, product, and TS structures, fixed partitions, and five evaluation metrics for reaction barrier prediction. We further propose a simple and effective strategy: pretrain a shared molecular encoder on reactants, products, and TS structures with barrier supervision, then reuse the encoder in a standard endpoint-only predictor. We evaluate direct TS use and TS-informed pretraining across four datasets and six encoders, test reaction-scaffold generalization, extend TS exposure to five additional pretraining strategies, and compare against six established barrier-prediction baselines. TS information is useful in every evaluated task, while TS-informed pretraining consistently improves endpoint-only prediction, transfers to unseen reaction-scaffold families, generalizes across other pretraining strategies, and achieves the best performance among the evaluated baseline methods.