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While various AI-based molecule generators have significantly advanced toward practical applications, their effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that assists users in designing new molecules using an AI-based molecule generator through only chat interactions, including automated construction of reward functions for the specified properties. Our study showcases the utility of ChatChemTS through de novo design cases involving chromophores and anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- and multiobjective molecule optimization scenarios, respectively. ChatChemTS is provided as an open-source package on GitHub at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/molecule-generator-collection\/ChatChemTS\" ext-link-type=\"uri\">https:\/\/github.com\/molecule-generator-collection\/ChatChemTS<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Scientific contribution<\/jats:bold>\n                    <\/jats:p>\n                    <jats:p>ChatChemTS is an open-source application that assists users in utilizing an AI-based molecule generator, ChemTSv2, solely through chat interactions. This study demonstrates that LLMs possess the potential to utilize advanced software, such as AI-based molecular generators, which require specialized knowledge and technical skills.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13321-025-00984-8","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T12:11:09Z","timestamp":1742818269000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Large language models open new way of AI-assisted molecule design for chemists"],"prefix":"10.1186","volume":"17","author":[{"given":"Shoichi","family":"Ishida","sequence":"first","affiliation":[]},{"given":"Tomohiro","family":"Sato","sequence":"additional","affiliation":[]},{"given":"Teruki","family":"Honma","sequence":"additional","affiliation":[]},{"given":"Kei","family":"Terayama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"984_CR1","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1021\/acscentsci.8b00213","volume":"4","author":"M Sumita","year":"2018","unstructured":"Sumita M, Yang X, Ishihara S, Tamura R, Tsuda K (2018) Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies. 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