Researchers are developing advanced large language models (LLMs) for chemical retrosynthesis, aiming to improve the prediction of synthetic routes for molecules. One approach, C3LM, utilizes a massive dataset of ~45.6 million reactions and a Top-K prompting strategy to generate diverse and plausible reaction predictions, achieving state-of-the-art results on benchmarks like URSA-expert-2026. Another method, RetroMPA, acts as a plug-and-play framework that enhances existing retrosynthesis models by incorporating chemical knowledge as priors, showing significant accuracy improvements on datasets like USPTO-50K. Additionally, ConfRetro leverages 3D conformer information and spatial structure within a Transformer-based model to achieve new state-of-the-art performance in template-free retrosynthesis. AI
IMPACT These advancements in LLMs for chemical retrosynthesis could accelerate drug discovery and organic synthesis planning.
RANK_REASON Multiple research papers detailing new models and frameworks for chemical retrosynthesis prediction.
- arXiv
- ConfRetro
- Hugging Face
- Jiaxi Zhuang
- RetroMPA
- USPTO-50K
- USPTO-Full
- C3LM
- ChemCensor
- CREED-CCV-2+USPTO-XL
- Top-K prompting
- URSA-expert-2026
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