Authors
Tianhao Su, Xitao Wang, Musen Li, Guanhua Qin, Shunbo Hu, Tong-Yi Zhang
Published in
Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e76827. Jul 29, 2026. Epub Jul 29, 2026.
Abstract
One-step retrosynthesis prediction is fundamentally limited by the random training order of sequence-to-sequence models and the inherent mismatch between local text generation and global chemical topology. Here we present CLRe (Contrastive curriculum Learning for Retrosynthesis), a framework that integrates self-supervised curriculum learning with topological buffering to resolve these bottlenecks. We introduce a label-free contrastive metric that quantifies intrinsic molecular complexity to optimize training pacing. Furthermore, we adapt label smoothing to act as a topological buffer, which preserves the search entropy required for complex multi-path chemical reasoning. We demonstrate that CLRe consistently improves performance on the USPTO-50K and USPTO-MIT datasets, significantly reducing accuracy disparities across historically challenging reaction classes. By capturing fine-grained structural complexity orthogonal to standard reaction rules, CLRe offers a robust strategy for bridging data-driven sequence generation with intrinsic chemical intuition.
PMID:
42525235
Bibliographic data and abstract were imported from PubMed on 29 Jul 2026.
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