{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:16:26Z","timestamp":1772280986062,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-00988-4","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T11:10:38Z","timestamp":1746702638000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023"],"prefix":"10.1186","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6510-3666","authenticated-orcid":false,"given":"Murat","family":"Ko\u00e7ak","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2473-4431","authenticated-orcid":false,"given":"Zafer","family":"Ak\u00e7al\u0131","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"988_CR1","doi-asserted-by":"publisher","unstructured":"Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry\u2019s grand challenge. Nat Rev Drug Discov 9(3):203\u2013214. https:\/\/doi.org\/10.1038\/nrd3078","DOI":"10.1038\/nrd3078"},{"key":"988_CR2","doi-asserted-by":"publisher","unstructured":"Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241\u20131250. https:\/\/doi.org\/10.1016\/j.drudis.2018.01.039","DOI":"10.1016\/j.drudis.2018.01.039"},{"key":"988_CR3","doi-asserted-by":"publisher","unstructured":"Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Pocock M (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18(6):463\u2013477. https:\/\/doi.org\/10.1038\/s41573-019-0024-5","DOI":"10.1038\/s41573-019-0024-5"},{"key":"988_CR4","unstructured":"McCarthy J, Minsky ML, & ML N. (1956). Rochester, and CE Shannon (1955). Proposal for the 1956 Dartmouth Summer Research Project on Artificial Intelligence."},{"key":"988_CR5","unstructured":"Buchanan BG, Shortliffe EH (1984) Rule based expert systems: the mycin experiments of the stanford heuristic programming project (the Addison-Wesley series in artificial intelligence). Addison-Wesley Longman Publishing Co. Inc, Reading"},{"key":"988_CR6","doi-asserted-by":"publisher","unstructured":"Mak KK, Pichika MR (2019) AI in drug development: present status and future prospects. Drug Discov Today 24(3):773\u2013780. https:\/\/doi.org\/10.1016\/j.drudis.2018.11.014","DOI":"10.1016\/j.drudis.2018.11.014"},{"key":"988_CR7","doi-asserted-by":"publisher","unstructured":"Lyu J, Wang S, Balius TE, Singh I, Levit A, Torabkhani Y, Yang X (2019) Ultra-large library docking for discovering new chemotypes. Nature 566(7743):224\u2013229. https:\/\/doi.org\/10.1038\/s41586-019-0917-9","DOI":"10.1038\/s41586-019-0917-9"},{"key":"988_CR8","doi-asserted-by":"publisher","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and AI. Nat Med 25:44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","DOI":"10.1038\/s41591-018-0300-7"},{"key":"988_CR9","doi-asserted-by":"publisher","unstructured":"Chan HS, Shan H, Dahoun T, Vogel H, Yuan S (2019) Advancing drug discovery via AI. Trends Pharmacol Sci 40(8):592\u2013604. https:\/\/doi.org\/10.1016\/j.tips.2019.05.004","DOI":"10.1016\/j.tips.2019.05.004"},{"key":"988_CR10","doi-asserted-by":"publisher","unstructured":"Tran BX, Nghiem S, Sahin O, Vu TM, Ha GH, Vu GT, Ho CS (2021) Artificial intelligence in drug discovery: Bibliometric analysis of its research development. J Inf 15(3):101566. https:\/\/doi.org\/10.1016\/j.joi.2021.101566","DOI":"10.1016\/j.joi.2021.101566"},{"key":"988_CR11","doi-asserted-by":"publisher","unstructured":"Zhang L, Zhang Y, Chen X, Zhang H (2022) A bibliometric analysis of artificial intelligence in drug discovery and development from 2000 to 2021. Expert Opin Drug Discov 17(8):813\u2013824. https:\/\/doi.org\/10.1080\/17460441.2022.2096016","DOI":"10.1080\/17460441.2022.2096016"},{"key":"988_CR12","doi-asserted-by":"publisher","unstructured":"Aria M, Cuccurullo C (2017) bibliometrix: an R tool for comprehensive science mapping analysis. J Informet 11(4):959\u2013975. https:\/\/doi.org\/10.1016\/j.joi.2017.08.007","DOI":"10.1016\/j.joi.2017.08.007"},{"key":"988_CR13","doi-asserted-by":"publisher","unstructured":"Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct bibliometric analysis? An overview and guidelines. J Bus Res 133:285\u2013296. https:\/\/doi.org\/10.1016\/j.jbusres.2021.04.070","DOI":"10.1016\/j.jbusres.2021.04.070"},{"key":"988_CR14","doi-asserted-by":"publisher","unstructured":"Cobo MJ, L\u00f3pez-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the fuzzy sets theory field. J Informet 5(1):146\u2013166. https:\/\/doi.org\/10.1016\/j.joi.2010.10.002","DOI":"10.1016\/j.joi.2010.10.002"},{"key":"988_CR15","unstructured":"Russell SJ, Norvig P (2022) AI: a modern approach (4th ed. Global edition. ed.). Pearson Education Limited."},{"key":"988_CR16","doi-asserted-by":"publisher","unstructured":"Ko\u00e7ak M, Ak\u00e7al\u0131 Z. Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022. Discov Onc 15, 566 (2024). https:\/\/doi.org\/10.1007\/s12672-024-01415-0","DOI":"10.1007\/s12672-024-01415-0"},{"key":"988_CR17","doi-asserted-by":"publisher","unstructured":"Pacurari AC, Bhattarai S, Muhammad A, Avram C, Mederle AO, Rosca O, Mavrea A (2023) Diagnostic accuracy of machine learning AI architectures in the detection and classification of lung cancer: a systematic review. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13132145","DOI":"10.3390\/diagnostics13132145"},{"key":"988_CR18","doi-asserted-by":"publisher","unstructured":"Huang MH, Rust RT (2018) AI in service. J Serv Res 21(2):155\u2013172. https:\/\/doi.org\/10.1177\/1094670517752459","DOI":"10.1177\/1094670517752459"},{"key":"988_CR19","doi-asserted-by":"publisher","unstructured":"Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255\u2013260. https:\/\/doi.org\/10.1126\/science.aaa8415","DOI":"10.1126\/science.aaa8415"},{"key":"988_CR20","doi-asserted-by":"publisher","unstructured":"Yang X, Wang Y, Byrne R, Schneider G, Yang S (2020) The landscape of artificial intelligence in drug development: bibliometric analysis of trends in 2014\u20132019. Front Pharmacol 11:1551. https:\/\/doi.org\/10.3389\/fphar.2020.01551","DOI":"10.3389\/fphar.2020.01551"},{"key":"988_CR21","doi-asserted-by":"crossref","unstructured":"Snyder H (2019) Literature review as a research methodology: An overview and guidelines. J Bus Res 104:333\u2013339","DOI":"10.1016\/j.jbusres.2019.07.039"},{"key":"988_CR22","doi-asserted-by":"publisher","unstructured":"Hood WW, Wilson CS (2001) Bibliometrics, scientometrics and informetrics literature [Review]. Scientometrics 52(2):291\u2013314. https:\/\/doi.org\/10.1023\/a:1017919924342","DOI":"10.1023\/a:1017919924342"},{"key":"988_CR23","doi-asserted-by":"publisher","unstructured":"Broadus RN (1987) Towards a definition of bibliometrics. Scientometrics 12(5\u20136):373\u2013379. https:\/\/doi.org\/10.1007\/bf02016680","DOI":"10.1007\/bf02016680"},{"key":"988_CR24","unstructured":"Andres A (2009) Measuring academic research: how to conduct a bibliometric study. Measuring academic research: how to conduct a bibliometric study. Chandos Publishing, Kidlington, pp 1\u2013169"},{"key":"988_CR25","doi-asserted-by":"publisher","unstructured":"Mart\u00edn-Mart\u00edn A, Orduna-Malea E, L\u00f3pez-C\u00f3zar ED (2018) A new method to delineate academic disciplines through Google Scholar Citations: the case of bibliometrics. Scientometrics 114(3):1251\u20131273. https:\/\/doi.org\/10.1007\/s11192-017-2587-4","DOI":"10.1007\/s11192-017-2587-4"},{"key":"988_CR26","unstructured":"WoS CC (2022).\u00a0https:\/\/www.webofscience.com\/wos\/woscc\/summary\/d395368a-6372-4770-ba65-01c2a114dea3-b5fc6c6e\/times-cited-descending\/1. Accessed 21 Nov 2023."},{"key":"988_CR27","unstructured":"InCites (2023). InCites.\u00a0https:\/\/incites.clarivate.com\/. Accessed 21 Nov 2023."},{"key":"988_CR28","unstructured":"Bibliometrix (2022). Bibliometrix. https:\/\/bibliometrix.org\/biblioshiny\/biblioshiny1.html. Accessed 21 Nov 2023."},{"key":"988_CR29","doi-asserted-by":"publisher","unstructured":"Van Eck NJ, Waltman L (2010) Software research: VOSviewer, a computer programme for bibliometric mapping. Scientometrics 84(2):523\u2013538. https:\/\/doi.org\/10.1007\/s11192-009-0146-3","DOI":"10.1007\/s11192-009-0146-3"},{"key":"988_CR30","unstructured":"Laskaris R (2015) AI: a modern approach 3rd ed. Libr J 140(6):45\u201345"},{"key":"988_CR31","doi-asserted-by":"publisher","unstructured":"Rowlands I (2005) Emerald authorship data, Lotka\u2019s law and research productivity. ASLIB Proc 57(1):5\u201310. https:\/\/doi.org\/10.1108\/00012530510579039","DOI":"10.1108\/00012530510579039"},{"key":"988_CR32","doi-asserted-by":"publisher","unstructured":"Ballester PJ, Mitchell JB,  Boobis AR. (2010). A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9), 1169\u20131175. https:\/\/doi.org\/10.1093\/bioinformatics\/btq112","DOI":"10.1093\/bioinformatics\/btq112"},{"key":"988_CR33","doi-asserted-by":"publisher","unstructured":"LeCun Y, Bengio Y, Hinton G. (2015). Deep learning. Nature, 521(7553), 436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"988_CR34","doi-asserted-by":"crossref","unstructured":"\u00d6zt\u00fcrk, H., \u00d6zg\u00fcr, A., & Ozkirimli, E. (2018). DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics, 34(17), i821\u2013i829. https:\/\/doi.org\/10.1093\/bioinformatics\/bty593","DOI":"10.1093\/bioinformatics\/bty593"},{"key":"988_CR35","doi-asserted-by":"crossref","unstructured":"Kaur A, Gulati S, Sharma R, Sinhababu A, Chakravarty R (2022) Visual citation navigation of open educational resources using Litmaps. Libr Hi Tech News 39(5):7\u201311","DOI":"10.1108\/LHTN-01-2022-0012"},{"key":"988_CR36","doi-asserted-by":"publisher","unstructured":"Schneider G. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4(8), 649\u2013663. https:\/\/doi.org\/10.1038\/nrd1799","DOI":"10.1038\/nrd1799"},{"key":"988_CR37","unstructured":"Garfield E (1980) Bradford\u2019s law and related statistical patterns. Curr Contents 19:5\u201312"},{"key":"988_CR38","doi-asserted-by":"publisher","unstructured":"Radhakrishnan S, Erbis S, Isaacs JA, Kamarthi S (2017) Novel keyword co-occurrence network-based methods for promoting systematic reviews of scientific literature. PLoS ONE 12(3):e0172778. https:\/\/doi.org\/10.1371\/journal.pone.0172778","DOI":"10.1371\/journal.pone.0172778"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00988-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-00988-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00988-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T11:10:46Z","timestamp":1746702646000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-00988-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["988"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-00988-4","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"12 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No ethical approval was needed because this is not a human study, but only online information was used.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"None.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"71"}}