这个模型把反应物和产物揉进一张图，用对比学习预训练，多个产率预测任务上超过现有基线，做反应预测的值得看看。
RxnCLF是一种自监督对比学习框架，基于压缩反应图（CRG）统一反应物与产物信息，显式学习反应中心及侧链上下文。该模型在170万条Pistachio反应数据上预训练，得到紧凑连续且化学可解释的隐空间。在Buchwald-Hartwig、钯催化BH偶联及专有HTE C-N偶联和酰胺形成等产率预测基准上，RxnCLF一致优于图和序列基线，R2指标显著提升。
RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction
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.