Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866915249004740608 |
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| author | Chen, Kexin Lu, Jiamin Li, Junyou Yang, Xiaoran Du, Yuyang Wang, Kunyi Shi, Qiannuan Yu, Jiahui Li, Lanqing Qiu, Jiezhong Pan, Jianzhang Huang, Yi Fang, Qun Heng, Pheng Ann Chen, Guangyong |
| author_facet | Chen, Kexin Lu, Jiamin Li, Junyou Yang, Xiaoran Du, Yuyang Wang, Kunyi Shi, Qiannuan Yu, Jiahui Li, Lanqing Qiu, Jiezhong Pan, Jianzhang Huang, Yi Fang, Qun Heng, Pheng Ann Chen, Guangyong |
| contents | Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis with retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions. To begin with, as an emulation on how chemical experts solve the RCO task, Chemist-X utilizes a novel RAG scheme to interrogate available molecular and literature databases to narrow the searching space for later processing. The agent then leverages a computer-aided design (CAD) tool we have developed through a large language model (LLM) supervised programming interface. With updated chemical knowledge obtained via RAG, as well as the ability in using CAD tools, our agent significantly outperforms conventional RCO AIs confined to the fixed knowledge within its training data. Finally, Chemist-X interacts with the physical world through an automated robotic system, which can validate the suggested chemical reaction condition without human interventions. The control of the robotic system was achieved with a novel algorithm we have developed for the equipment, which relies on LLMs for reliable script generation. Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10776 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis Chen, Kexin Lu, Jiamin Li, Junyou Yang, Xiaoran Du, Yuyang Wang, Kunyi Shi, Qiannuan Yu, Jiahui Li, Lanqing Qiu, Jiezhong Pan, Jianzhang Huang, Yi Fang, Qun Heng, Pheng Ann Chen, Guangyong Information Retrieval Artificial Intelligence Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis with retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions. To begin with, as an emulation on how chemical experts solve the RCO task, Chemist-X utilizes a novel RAG scheme to interrogate available molecular and literature databases to narrow the searching space for later processing. The agent then leverages a computer-aided design (CAD) tool we have developed through a large language model (LLM) supervised programming interface. With updated chemical knowledge obtained via RAG, as well as the ability in using CAD tools, our agent significantly outperforms conventional RCO AIs confined to the fixed knowledge within its training data. Finally, Chemist-X interacts with the physical world through an automated robotic system, which can validate the suggested chemical reaction condition without human interventions. The control of the robotic system was achieved with a novel algorithm we have developed for the equipment, which relies on LLMs for reliable script generation. Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories. |
| title | Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2311.10776 |