Retro-Expert framework enhances chemical synthesis with interpretable AI

Researchers have developed Retro-Expert, a new framework for retrosynthesis prediction that combines large language models (LLMs) with specialized models through reinforcement learning. This approach aims to overcome the limitations of static pattern-matching methods by enabling collaborative reasoning and providing interpretable, chemically grounded explanations. Experiments indicate that Retro-Expert outperforms existing methods and enhances trust among chemists by offering a clear reasoning path for its predictions. AI

IMPACT Enhances interpretability and trust in AI for chemical synthesis, potentially accelerating drug discovery and materials science.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new AI framework for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• large-language models
• Retro-Expert
• ScienceCast
• Xinyi Li

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