Margin-Calibrated Classifier Guidance for Property-Driven Synthesis Planning

A research paper on arXiv (2605.13101) introduces Sequence Completion Ranking (SCR), a novel method for property-driven synthesis planning in chemistry. Synthesis planning involves generating efficient reaction sequences to produce target molecules using pretrained single-step retrosynthesis models. The authors identify that auxiliary classifiers trained with cross-entropy loss fail to override token-level distributions from sparse reaction datasets. SCR uses contrastive argumentation and margin-based loss to calibrate classifiers, enabling meaningful discrimination during decoding without retraining the generator. This approach improves constraint satisfaction and chemist preferences in reaction planning.