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

• URL: http://arxiv.org/abs/2608.06259v1
• Date: Thu, 06 Aug 2026 16:51:23 GMT
• Title: RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction
• Abstract summary: We propose reaction contrastive learning foundation (RxnCLF) for reaction representation learning.<n> RxnCLF is built on a condensed reaction graph (CRG) that unifies reactant and product information into a single graph.<n>Pretrained on 1.7 million Pistachio reactions, RxnCLF learns a compact and continuous latent space that captures both reaction-center features and broader side chain contexts.
• Score: 3.456440183560995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reaction yield prediction remains challenging because labeled data are scarce and reaction space is both combinatorially large and sparsely populated, limiting the generalization of existing reaction representations. String-, fingerprint-, and graph-based reaction encodings only partially capture chemical transformations, making accurate prediction difficult for reactions with complex substrates. We propose reaction contrastive learning foundation (RxnCLF), a self-supervised contrastive framework for reaction representation learning. RxnCLF is built on a condensed reaction graph (CRG) that unifies reactant and product information into a single graph, enabling the model to learn explicit and enriched transformation structure rather than disconnected graphs. Pretrained on 1.7 million Pistachio reactions, RxnCLF learns a compact and continuous latent space that captures both reaction-center features and broader side chain contexts, making it transformation-aware and chemically interpretable. Fine-tuned on multiple yield prediction benchmarks, including Buchwald-Hartwig, Pd-catalyzed BH coupling, and proprietary HTE C-N coupling and amide formation datasets, RxnCLF consistently outperforms graph and sequence-based baselines, improving R2 and achieving the best performance overall. Our results highlight the promise of CRG-based RxnCLF as a scalable reaction foundation model, with the potential to generalize across broader reaction spaces and support diverse downstream reaction informatics tasks, including regioselectivity prediction, enantioselectivity prediction, and reaction condition optimization.

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