What happened
Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transformations. A key challenge is to integrate this reaction information into chemical representations. Here we introduce LARK, a unified framework for learning atom-molecule-reaction knowledge from chemical transformations. Using approximately 0.6 million reactions encompassing 0.88 million unique molecular components, LARK combines bidirectional bond-electron reconstruction with structural and geometric supervision through a role-aware hierarchical hypergraph Transformer that links local atom representations to reaction context. The learned representations transfer to both reaction prediction and single-molecule property prediction, with strong performance across 42 tasks covering reaction outcomes, physicochemical properties, toxicity, metabolism and pharmacokinetics. Reactioncentre analyses show that LARK identifies where bonds break and form, while targeted interventions reveal that the learned atom representations contribute to reconstructing these changes. These findings indicate that LARK learns chemically informative representations of substructures involved in molecular transformations. Together, the results establish chemical transformations as a source of structured supervision for representation learning across molecular and reaction scales. Update (2026-09-21): PREpiBind: Protein Representation-integrated Epitope--MHC Class II Binding Prediction . Accurate prediction of peptide-MHC class II (pMHC-II) binding remains challenging because of extensive MHC polymorphism and context-dependent peptide recognition, and it is unclear which protein representation best captures these determinants when the downstream model is held fixed. Here, we present PREpiBind, a pMHC-II prediction method based on a dual-stream, joint-attention framework that integrates MHC and epitope representations. We integrated ten protein representations spanning substitution-matrix, structure-prediction-derived, and protein language model (PLM) families and evaluated them under a common downstream architecture and identical splits across Qualitative, mass spectrometry (MS), and thresholded IC50 datasets. Overall, PREpiBind with PLM representations yielded higher performance than the evaluated reference methods in the Qualitative and MS datasets, whereas NetMHCIIpan-4.3 was higher on the thresholded IC50 datasets using its binding-affinity head. In pooled evaluations, PLM representations yielded the highest ROC-AUC values among the tested represen