This work introduces Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions and establishes Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.
Abstract
Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions. We compile CREED-CCV-2+USPTO-XL, an ultra-large-scale dataset of ~45.6 million verified reactions to train the C3LM (Chemistry Constraint-Consistent Language Model). By integrating fine-tuning with ChemCensor-based and novelty-oriented rewards, our model achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark. Further analysis of reaction uniqueness shows that LLMs and conventional models explore complementary reaction spaces, motivating ensemble-based retrosynthesis systems. Overall, our results establish Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.
The proposed Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists, achieves state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5.
Weida Wang, Benteng Chen, Di Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global syn-thesizability knowledge into step-wise retrosyn-thetic prediction and improves planning accuracy and search efficiency under realistic retrosynthetic settings.
Yu-Jie Chen, Ajie Lin, Tengfei Ma et al.· Proceedings of the Thirty-Fi...· 0 citations
The URSA (Utilitarian RetroSynthesis Assessment) evaluation framework is introduced that provides the opportunity to benchmark the synthetic routes not only from a formal perspective, such as convergence to commercially available starting materials, but also from a chemical plausibility perspective, mimicking the way e...
B. Zagribelnyy, Ivan D. Ilin, N. Bondarev et al.· arXiv.org· 0 citations
These syntheses confirmed viable, previously unreported substrate combinations for established, classic reaction paradigms─specifically, the Suzuki-Miyaura coupling, the Bucherer reaction, and the Friedel-Crafts acylation, thereby suggesting that RetroMPA can operate beyond mere data fitting.
Mianzhi Liu, Fan Xiao, Zhi-Qiang Yu et al.· Journal of Chemical Informat...· 0 citations
Retrosynthesis, the process of predicting reactants from products, remains a critical challenge in computational chemistry and drug discovery. While recent deep learning methods have shown strong performance, they remain overly reliant on reaction datasets, which are limited in availability and quality. Large-scale unl...
Yujie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
A framework that systematically standardizes and integrates multiple reaction datasets into a high-quality, unique-structure-per-entity dataset, coupled with active learning to strategically expand chemical space is introduced, establishing a blueprint for robust machine learning in synthetic chemistry.
Paulo Neves, Bo Hao, Santeri Aikonen et al.· Nature Computational Science· 1 citation
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