| title | Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis |
|---|---|
| authors | Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Maksim Kuznetsov, Mathieu Reymond, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov |
| date | 2026-08-19 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.18940 |
| affiliation | Insilico Medicine |
| upvotes | 23 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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. |
cite
@misc{zagribelnyy2026training,
title={Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis},
author={Bogdan Zagribelnyy and Ivan Ilin and Nikita Bondarev and Maksim Kuznetsov and Mathieu Reymond and Vladimir Aladinskiy and Alex Aliper and Alex Zhavoronkov},
year={2026},
eprint={2608.18940},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.18940}
}