CLRe: A Synergistic Dual-Engine Framework for One-Step Retrosynthesis Prediction

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.