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arXiv AI By Jiaxing Sun, Xuli Shen, Jiang Wu, Ruiyi Ding, Ruikang Zhang, Xiao An, Xibao Li, Yubin Wang, Xingjian Wei, Yinfan Wang, Yu Liu, Lingli Ge, Xi Xue, Xiaojian Wang, Lijun Wu, Chang-Yu Hsieh, Yuqiang Li, Bowen Zhou, Dahua Lin, Conghui He

Reaction precedents extend retrosynthetic prediction beyond training coverage

Read the original on arXiv AI →

The study demonstrates that using reaction precedents during inference can significantly improve retrosynthetic prediction accuracy, even when the model’s training data is limited or outdated. By expanding only the retrieval corpus, the authors achieved a Top‑1 accuracy increase from 13.0% to 58.0% for transformations absent from training, and from 4.9% to 23.1% for temporally held‑out data. Across 14 general‑purpose models, precedents boosted performance on both test sets and USPTO‑50K, surpassing specialized models without any task‑specific fine‑tuning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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