C3LM targets single-step retrosynthesis with Top-K prompting
Researchers train a chemistry LLM on ~45.6M reactions for more diverse synthesis predictions.
Why it matters
The paper points to evaluation and training methods that reward multiple plausible chemical routes instead of single-answer matches. Its finding that LLMs and conventional models explore complementary reaction spaces supports ensemble approaches for AI-assisted synthesis planning.
The key points
- 1.C3LM was trained on ~45.6M verified reactions.
- 2.Top-K prompting targets diverse plausible retrosynthesis outputs.
- 3.LLMs and conventional models showed complementary reaction spaces.
Researchers introduced C3LM, the Chemistry Constraint-Consistent Language Model, for single-step retrosynthesis. The work uses Top-K prompting to better reflect the one-to-many nature of reaction prediction and trains on CREED-CCV-2+USPTO-XL, a dataset of about 45.6 million verified reactions. The authors report state-of-the-art performance on the out-of-distribution URSA-expert-2026 benchmark using fine-tuning with ChemCensor-based and novelty-oriented rewards.
⚡ Try this today
If you build retrosynthesis tools, evaluate Top-K plausible outputs and compare LLM predictions against conventional model ensembles.
Sources & original reporting
This brief summarizes and links to reporting from the publishers below.
- arXiv cs.LGTraining Chemical Plausibility-Aware Large Language Models for Single-Step RetrosynthesisAug 20, 12:00 PM↗
- arXiv cs.AITraining Chemical Plausibility-Aware Large Language Models for Single-Step RetrosynthesisAug 20, 12:00 PM↗
- arXiv cs.CLTraining Chemical Plausibility-Aware Large Language Models for Single-Step RetrosynthesisAug 20, 12:00 PM↗
- HF Daily PapersTraining Chemical Plausibility-Aware Large Language Models for Single-Step RetrosynthesisAug 19, 4:00 AM↗
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