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Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

Research Bioinformatics AI

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TL;DR - C3LM is a chemical-plausibility-aware language model for single-step retrosynthesis, trained on roughly 45.6 million verified reactions with a Top-K prediction paradigm. It achieves state-of-the-art results on an out-of-distribution benchmark and supports more diverse, plausible synthesis planning.

  • Top-K prompting addresses retrosynthesis’s one-to-many nature by producing multiple plausible reaction predictions rather than optimizing for one answer.
  • Training combines fine-tuning with ChemCensor-based chemical-plausibility rewards and novelty-oriented rewards.
  • C3LM achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark.
  • LLMs and conventional models explore complementary reaction spaces, suggesting potential gains from ensemble systems.

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Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

arXiv cs.LG Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Maksim Kuznetsov, Mathieu Reymond, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov 2026-08-19 arXiv:2608.18940
Public signals Hugging Face upvotes 35
Providers: Hugging Face · Upvotes 35 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-19 14:26:39.427114 UTC

TL;DR - C3LM is a chemical-plausibility-aware language model for single-step retrosynthesis, trained on roughly 45.6 million verified reactions with a Top-K prediction paradigm. It achieves state-of-the-art results on an out-of-distribution benchmark and supports more diverse, plausible synthesis planning.

  • Top-K prompting addresses retrosynthesis’s one-to-many nature by producing multiple plausible reaction predictions rather than optimizing for one answer.
  • Training combines fine-tuning with ChemCensor-based chemical-plausibility rewards and novelty-oriented rewards.
  • C3LM achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark.
  • LLMs and conventional models explore complementary reaction spaces, suggesting potential gains from ensemble systems.
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