RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction

TL;DR - RxnCLF is a self-supervised contrastive foundation model for chemical reaction representation, built on condensed reaction graphs and pretrained on 1.7M reactions, that improves yield prediction where labeled data are scarce.

• Uses a condensed reaction graph (CRG) merging reactants and products into one graph, so the model encodes the transformation itself rather than disconnected reactant/product structures.
• Pretrained contrastively on 1.7M Pistachio reactions, yielding a compact continuous latent space capturing reaction-center features plus broader side-chain context, described as transformation-aware and chemically interpretable.
• Fine-tuned on Buchwald-Hartwig, Pd-catalyzed BH coupling, and proprietary HTE C–N coupling and amide formation benchmarks, it beats graph- and sequence-based baselines on R².
• Authors position it as a general reaction foundation model extensible to regio-/enantioselectivity prediction and reaction condition optimization; no quantitative numbers are given in the abstract.