{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T03:38:32Z","timestamp":1784345912592,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-025-00849-y","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T09:03:05Z","timestamp":1755680585000},"page":"962-972","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2900-7313","authenticated-orcid":false,"given":"Keyan","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1636-5269","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-7442","authenticated-orcid":false,"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"849_CR1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1038\/s42254-023-00581-4","volume":"5","author":"A Birhane","year":"2023","unstructured":"Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nat. Rev. Phys. 5, 277\u2013280 (2023).","journal-title":"Nat. Rev. Phys."},{"key":"849_CR2","first-page":"68539","volume":"36","author":"T Schick","year":"2023","unstructured":"Schick, T. et al. Toolformer: language models can teach themselves to use tools. Adv. Neural Inf. Process. Syst. 36, 68539\u201368551 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR3","first-page":"71995","volume":"36","author":"R Yang","year":"2024","unstructured":"Yang, R. et al. GPT4Tools: teaching large language model to use tools via self-instruction. Adv. Neural Inf. Process. Syst. 36, 71995\u201372007 (2024).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR4","first-page":"59662","volume":"36","author":"T Guo","year":"2023","unstructured":"Guo, T. et al. What can large language models do in chemistry? A comprehensive benchmark on eight tasks. Adv. Neural Inf. Process. Syst. 36, 59662\u201359688 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR5","unstructured":"Zhao, W. X. et al. A survey of large language models. Preprint at https:\/\/arxiv.org\/abs\/2303.18223 (2023)."},{"key":"849_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605943","volume":"56","author":"B Min","year":"2023","unstructured":"Min, B. et al. Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput. Surveys 56, 1\u201340 (2023).","journal-title":"ACM Comput. Surveys"},{"key":"849_CR7","doi-asserted-by":"publisher","first-page":"186345","DOI":"10.1007\/s11704-024-40231-1","volume":"18","author":"L Wang","year":"2024","unstructured":"Wang, L. et al. A survey on large language model based autonomous agents. Front. Comput. Sci. 18, 186345 (2024).","journal-title":"Front. Comput. Sci."},{"key":"849_CR8","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1039\/D4SC03921A","volume":"16","author":"MC Ramos","year":"2025","unstructured":"Ramos, M. C., Collison, C. J. & White, A. D. A review of large language models and autonomous agents in chemistry. Chem. Sci. 16, 2514\u20132572 (2025).","journal-title":"Chem. Sci."},{"key":"849_CR9","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1038\/s41586-023-06792-0","volume":"624","author":"DA Boiko","year":"2023","unstructured":"Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570\u2013578 (2023).","journal-title":"Nature"},{"key":"849_CR10","doi-asserted-by":"crossref","unstructured":"Janakarajan, N., Erdmann, T., Swaminathan, S., Laino, T. & Born, J. Language models in molecular discovery. In Drug Development Supported by Informatics (eds Satoh, H. et al.) 121\u2013141 (Springer, 2024).","DOI":"10.1007\/978-981-97-4828-0_7"},{"key":"849_CR11","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s42256-024-00832-8","volume":"6","author":"AM Bran","year":"2024","unstructured":"Bran, A. M. et al. Augmenting large language models with chemistry tools. Nat. Mach. Intell. 6, 525\u2013535 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"849_CR12","doi-asserted-by":"publisher","first-page":"46563","DOI":"10.1021\/acsomega.4c08408","volume":"9","author":"AD McNaughton","year":"2024","unstructured":"McNaughton, A. D. et al. CACTUS: chemistry agent connecting tool usage to science. ACS Omega 9, 46563\u201346573 (2024).","journal-title":"ACS Omega"},{"key":"849_CR13","doi-asserted-by":"publisher","first-page":"btae075","DOI":"10.1093\/bioinformatics\/btae075","volume":"40","author":"Q Jin","year":"2024","unstructured":"Jin, Q., Yang, Y., Chen, Q. & Lu, Z. GeneGPT: augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics 40, btae075 (2024).","journal-title":"Bioinformatics"},{"key":"849_CR14","unstructured":"Huang, K. et al. CRISPR-GPT: an LLM agent for automated design of gene-editing experiments. Preprint at https:\/\/arxiv.org\/abs\/2404.18021 (2024)."},{"key":"849_CR15","unstructured":"Liu, H. & Wang, H. GenoTEX: a benchmark for evaluating LLM-based exploration of gene expression data in alignment with bioinformaticians. Preprint at https:\/\/arxiv.org\/abs\/2406.15341 (2024)."},{"key":"849_CR16","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1039\/D4DD00013G","volume":"3","author":"A Ghafarollahi","year":"2024","unstructured":"Ghafarollahi, A. & Buehler, M. J. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digital Discovery 3, 1389\u20131409 (2024).","journal-title":"Digital Discovery"},{"key":"849_CR17","unstructured":"Jia, S., Zhang, C. & Fung, V. LLMatDesign: autonomous materials discovery with large language models. Preprint at https:\/\/arxiv.org\/abs\/2406.13163 (2024)."},{"key":"849_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-48998-4","volume":"15","author":"Y Kang","year":"2024","unstructured":"Kang, Y. & Kim, J. ChatMOF: an artificial intelligence system for predicting and generating metal\u2013organic frameworks using large language models. Nat. Commun. 15, 4705 (2024).","journal-title":"Nat. Commun."},{"key":"849_CR19","doi-asserted-by":"publisher","first-page":"3184","DOI":"10.1109\/TCAD.2024.3383347","volume":"43","author":"H Wu","year":"2024","unstructured":"Wu, H. et al. ChatEDA: a large language model powered autonomous agent for EDA. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 43, 3184\u20133197 (2024).","journal-title":"IEEE Trans. Comput.-Aided Design Integr. Circuits Syst."},{"key":"849_CR20","doi-asserted-by":"publisher","first-page":"102131","DOI":"10.1016\/j.eml.2024.102131","volume":"67","author":"B Ni","year":"2024","unstructured":"Ni, B. & Buehler, M. J. MechAgents: large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge. Extreme Mech. Lett. 67, 102131 (2024).","journal-title":"Extreme Mech. Lett."},{"key":"849_CR21","unstructured":"Yao, S. et al. ReAct: synergizing reasoning and acting in language models. In International Conference on Learning Representations https:\/\/iclr.cc\/virtual\/2023\/oral\/12647 (2023)."},{"key":"849_CR22","unstructured":"He, J. et al. Control risk for potential misuse of artificial intelligence in science. Preprint at https:\/\/arxiv.org\/abs\/2312.06632 (2023)."},{"key":"849_CR23","unstructured":"Liu, X. et al. ToolNet: connecting large language models with massive tools via tool graph. Preprint at https:\/\/arxiv.org\/abs\/2403.00839 (2024)."},{"key":"849_CR24","first-page":"45870","volume":"36","author":"S Hao","year":"2024","unstructured":"Hao, S., Liu, T., Wang, Z. & Hu, Z. ToolkenGPT: augmenting frozen language models with massive tools via tool embeddings. Adv. Neural Inf. Process. Syst. 36, 45870\u201345894 (2024).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR25","first-page":"8634","volume":"36","author":"N Shinn","year":"2024","unstructured":"Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. & Yao, S. Reflexion: language agents with verbal reinforcement learning. Adv. Neural Inf. Process. Syst. 36, 8634\u20138652 (2024).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR26","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1038\/s41586-023-06728-8","volume":"623","author":"JB Ingraham","year":"2023","unstructured":"Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070\u20131078 (2023).","journal-title":"Nature"},{"key":"849_CR27","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123\u20131130 (2023).","journal-title":"Science"},{"key":"849_CR28","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/S0006-3495(01)76033-X","volume":"80","author":"AR Atilgan","year":"2001","unstructured":"Atilgan, A. R. et al. Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80, 505\u2013515 (2001).","journal-title":"Biophys. J."},{"key":"849_CR29","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1093\/bioinformatics\/btr168","volume":"27","author":"A Bakan","year":"2011","unstructured":"Bakan, A., Meireles, L. M. & Bahar, I. Prody: protein dynamics inferred from theory and experiments. Bioinformatics 27, 1575\u20131577 (2011).","journal-title":"Bioinformatics"},{"key":"849_CR30","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","volume":"25","author":"PJ Cock","year":"2009","unstructured":"Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422 (2009).","journal-title":"Bioinformatics"},{"key":"849_CR31","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1021\/acscentsci.9b00576","volume":"5","author":"P Schwaller","year":"2019","unstructured":"Schwaller, P. et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Central Science 5, 1572\u20131583 (2019).","journal-title":"ACS Central Science"},{"key":"849_CR32","doi-asserted-by":"crossref","unstructured":"Pei, Q. et al. BioT5+: towards generalized biological understanding with IUPAC integration and multi-task tuning. In Findings of the Association for Computational Linguistics: ACL 2024, 1216\u20131240 (Association for Computational Linguistics, 2024).","DOI":"10.18653\/v1\/2024.findings-acl.71"},{"key":"849_CR33","doi-asserted-by":"publisher","first-page":"D1220","DOI":"10.1093\/nar\/gkv1253","volume":"44","author":"G Papadatos","year":"2016","unstructured":"Papadatos, G. et al. SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res. 44, D1220\u2013D1228 (2016).","journal-title":"Nucleic Acids Res."},{"key":"849_CR34","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2016","unstructured":"Kim, S. et al. Pubchem substance and compound databases. Nucleic Acids Res. 44, D1202\u2013D1213 (2016).","journal-title":"Nucleic Acids Res."},{"key":"849_CR35","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1021\/acs.jced.2c00583","volume":"68","author":"NS Bobbitt","year":"2023","unstructured":"Bobbitt, N. S. et al. MOFX-DB: an online database of computational adsorption data for nanoporous materials. J. Chem. Eng. Data 68, 483\u2013498 (2023).","journal-title":"J. Chem. Eng. Data"},{"key":"849_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01181-0","volume":"9","author":"A Nandy","year":"2022","unstructured":"Nandy, A. et al. Mofsimplify, machine learning models with extracted stability data of three thousand metal\u2013organic frameworks. Sci. Data 9, 74 (2022).","journal-title":"Sci. Data"},{"key":"849_CR37","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1080\/08927022.2015.1010082","volume":"42","author":"D Dubbeldam","year":"2016","unstructured":"Dubbeldam, D., Calero, S., Ellis, D. E. & Snurr, R. Q. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Molecular Simulation 42, 81\u2013101 (2016).","journal-title":"Molecular Simulation"},{"key":"849_CR38","unstructured":"BLAST: basic local alignment search tool. NIH https:\/\/blast.ncbi.nlm.nih.gov (2024)."},{"key":"849_CR39","unstructured":"RDKit: open-source cheminformatics software. RDKit http:\/\/www.rdkit.org (2024)."},{"key":"849_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-015-0069-3","volume":"7","author":"D Bajusz","year":"2015","unstructured":"Bajusz, D., R\u00e1cz, A. & H\u00e9berger, K. Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminformatics 7, 1\u201313 (2015).","journal-title":"J. Cheminformatics"},{"key":"849_CR41","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","volume":"147","author":"TF Smith","year":"1981","unstructured":"Smith, T. F. et al. Identification of common molecular subsequences. J. Mol. Biol. 147, 195\u2013197 (1981).","journal-title":"J. Mol. Biol."},{"key":"849_CR42","unstructured":"Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In International Conference on Learning Representations https:\/\/iclr.cc\/virtual\/2022\/poster\/6319 (2022)."},{"key":"849_CR43","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998\u20136008 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"849_CR44","doi-asserted-by":"publisher","unstructured":"Yu, J. Dataset for the paper \u201cSciToolAgent: A knowledge graph-driven scientific agent for multi-tool integration\u201d. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15691499 (2025).","DOI":"10.5281\/zenodo.15691499"},{"key":"849_CR45","doi-asserted-by":"publisher","unstructured":"Yu, J. & Ding, K. HICAI-ZJU\/SciToolAgent: V1.0.1. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15707182 (2025).","DOI":"10.5281\/zenodo.15707182"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00849-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T03:03:17Z","timestamp":1760065397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00849-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["849"],"URL":"https:\/\/doi.org\/10.1038\/s43588-025-00849-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5610718\/v1","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"9 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}