ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large language models

Ziyang Gong, Chengwei Zhang, Danyang Song, Weida Xia, Bin Shen, Weike Su, Hongliang Duan, An Su

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce ChemReactSeek, an advanced artificial intelligence platform that integrates retrieval-augmented generation using large language models (LLMs) to automate the design of chemical reaction protocols. The system employs DeepSeek-v3 to extract and structure data from scientific literature, enabling the construction of a specialized knowledge base focused on hydrogenation reactions. By combining FAISS-based semantic search with LLM-driven reasoning, ChemReactSeek generates executable reaction conditions, which we further validate through experiments on heterogeneous hydrogenation.

Original languageEnglish
Pages (from-to)13137-13140
Number of pages4
JournalChemical Communications
Volume61
Issue number70
DOIs
Publication statusPublished - 26 Aug 2025

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