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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Maksim Kuznetsov, Mathieu Reymond, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov
• Venue: arXiv: cs.LG
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18940v1
• PDF: https://arxiv.org/pdf/2608.18940v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

Researchers introduce a new method called Top-K prompting to train language models for single-step retrosynthesis, a crucial component of computer-aided synthesis planning. They use an ultra-large dataset of verified reactions to train their model, C3LM, which achieves state-of-the-art performance on a benchmark. The study suggests that this new method could lead to more robust future language model-based synthesis planning.

What the Document Says

• The researchers introduce Top-K prompting as a training and inference paradigm for single-step retrosynthesis.
• They compile a dataset, CREED-CCV-2+USPTO-XL, containing ~45.6 million verified reactions to train the C3LM model.
• The model integrates fine-tuning with ChemCensor-based and novelty-oriented rewards.
• C3LM achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark.
• The study shows that LLMs and conventional models explore different reaction spaces, suggesting ensemble-based retrosynthesis systems.

Key Concepts Mentioned

• single-step retrosynthesis
• Top-K prompting
• C3LM
• CREED-CCV-2+USPTO-XL
• OOD URSA-expert-2026
• LLMs
• conventional models
• ensemble-based retrosynthesis systems

Claims Made by the Document

• Top-K prompting is a robust training and inference paradigm for single-step retrosynthesis.
• C3LM achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark.

Evidence Used

• The authors describe Top-K prompting and its application in the text.
• The text states that C3LM achieves state-of-the-art performance on the given benchmark.

Methods / Approach

• Top-K prompting
• Compiling a large dataset
• Fine-tuning with ChemCensor-based and novelty-oriented rewards

Limitations Stated by the Authors

The study does not provide details about the specifics of the ChemCensor-based rewards or the novelty-oriented rewards.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations of the Top-K prompting approach in real-world applications or its scalability.

Open Questions

• What specifics are involved in the ChemCensor-based and novelty-oriented rewards used in fine-tuning?
• How does the Top-K prompting approach perform in real-world applications, and what are its potential scalability issues?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on introducing a new training method for single-step retrosynthesis and demonstrating its effectiveness, but it does not delve into the specifics of the rewards used or discuss potential limitations in practical applications.

Raw Abstract / Excerpt

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.