An automatic end-to-end chemical synthesis development platform powered by large language models
Abstract The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involv...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54457-x |
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| author | Yixiang Ruan Chenyin Lu Ning Xu Yuchen He Yixin Chen Jian Zhang Jun Xuan Jianzhang Pan Qun Fang Hanyu Gao Xiaodong Shen Ning Ye Qiang Zhang Yiming Mo |
| author_facet | Yixiang Ruan Chenyin Lu Ning Xu Yuchen He Yixin Chen Jian Zhang Jun Xuan Jianzhang Pan Qun Fang Hanyu Gao Xiaodong Shen Ning Ye Qiang Zhang Yiming Mo |
| author_sort | Yixiang Ruan |
| collection | DOAJ |
| description | Abstract The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF’s broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction). |
| format | Article |
| id | doaj-art-79f16f538fb04daeae430f83d0064274 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-79f16f538fb04daeae430f83d00642742024-11-24T12:32:23ZengNature PortfolioNature Communications2041-17232024-11-0115111610.1038/s41467-024-54457-xAn automatic end-to-end chemical synthesis development platform powered by large language modelsYixiang Ruan0Chenyin Lu1Ning Xu2Yuchen He3Yixin Chen4Jian Zhang5Jun Xuan6Jianzhang Pan7Qun Fang8Hanyu Gao9Xiaodong Shen10Ning Ye11Qiang Zhang12Yiming Mo13College of Chemical and Biological Engineering, Zhejiang UniversityZhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterCollege of Chemical and Biological Engineering, Zhejiang UniversityCollege of Chemical and Biological Engineering, Zhejiang UniversityCollege of Chemical and Biological Engineering, Zhejiang UniversityZhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and TechnologyChemical & Analytical Development, Suzhou Novartis Technical Development Co. Ltd.Rezubio Pharmaceuticals Co. Ltd.Zhejiang-Hong Kong Joint Laboratory for Intelligent Molecule and Material Design and Synthesis, ZJU-Hangzhou Global Scientific and Technological Innovation CenterCollege of Chemical and Biological Engineering, Zhejiang UniversityAbstract The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF’s broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction).https://doi.org/10.1038/s41467-024-54457-x |
| spellingShingle | Yixiang Ruan Chenyin Lu Ning Xu Yuchen He Yixin Chen Jian Zhang Jun Xuan Jianzhang Pan Qun Fang Hanyu Gao Xiaodong Shen Ning Ye Qiang Zhang Yiming Mo An automatic end-to-end chemical synthesis development platform powered by large language models Nature Communications |
| title | An automatic end-to-end chemical synthesis development platform powered by large language models |
| title_full | An automatic end-to-end chemical synthesis development platform powered by large language models |
| title_fullStr | An automatic end-to-end chemical synthesis development platform powered by large language models |
| title_full_unstemmed | An automatic end-to-end chemical synthesis development platform powered by large language models |
| title_short | An automatic end-to-end chemical synthesis development platform powered by large language models |
| title_sort | automatic end to end chemical synthesis development platform powered by large language models |
| url | https://doi.org/10.1038/s41467-024-54457-x |
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