{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:04:35Z","timestamp":1784750675590,"version":"3.55.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000057","name":"U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM151255"],"award-info":[{"award-number":["R35GM151255"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["NIH T32 GM133352-4"],"award-info":[{"award-number":["NIH T32 GM133352-4"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-024-00594-8","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T17:03:31Z","timestamp":1709053411000},"page":"96-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Characterizing emerging companies in computational drug development"],"prefix":"10.1038","volume":"4","author":[{"given":"Chloe","family":"Markey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Croset","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivia Ruth","family":"Woolley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can Martin","family":"Buldun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4741-4171","authenticated-orcid":false,"given":"Christian","family":"Koch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3388-3295","authenticated-orcid":false,"given":"Daniel","family":"Koller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4789-7380","authenticated-orcid":false,"given":"Daniel","family":"Reker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"594_CR1","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/s10822-020-00317-x","volume":"34","author":"N Brown","year":"2020","unstructured":"Brown, N. et al. Artificial intelligence in chemistry and drug design. J. Comput. Aided Mol. Des. 34, 709\u2013715 (2020).","journal-title":"J. Comput. Aided Mol. Des."},{"key":"594_CR2","unstructured":"Kopp, E. BioXCel Therapeutics announces FDA approval of IGALMI\u2122 (dexmedetomidine) sublingual film for acute treatment of agitation associated with schizophrenia or bipolar I or II disorder in adults. BioXcel Therapeutics (6 April 2022)."},{"key":"594_CR3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1038\/s41587-023-01788-7","volume":"41","author":"N Savage","year":"2023","unstructured":"Savage, N. Drug discovery companies are customizing ChatGPT: here\u2019s how. Nat. Biotechnol. 41, 585\u2013586 (2023).","journal-title":"Nat. Biotechnol."},{"key":"594_CR4","unstructured":"Joachim, B., Rehm, W., Ryan, S. & Wright, P. A biotech survival kit for a challenging public-market environment. McKinsey (19 September 2022)."},{"key":"594_CR5","unstructured":"Leclerc, O., Suhendra, M. & The, L. What are the biotech investment themes that will shape the industry? McKinsey (10 June 2022)."},{"key":"594_CR6","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/s41587-023-01661-7","volume":"41","author":"J Hodgson","year":"2023","unstructured":"Hodgson, J. 2022\u2014toughing out the trough. Nat. Biotechnol. 41, 159\u2013173 (2023).","journal-title":"Nat. Biotechnol."},{"key":"594_CR7","doi-asserted-by":"crossref","unstructured":"Prieto-Mart\u00ednez, F. D., L\u00f3pez-L\u00f3pez, E., Ju\u00e1rez-Mercado, K. E. & Medina-Franco, J. L. in In Silico Drug Design (ed. Roy, K.) 19\u201344 (Academic, 2019).","DOI":"10.1016\/B978-0-12-816125-8.00002-X"},{"key":"594_CR8","doi-asserted-by":"publisher","first-page":"5441","DOI":"10.1039\/C8SC00148K","volume":"9","author":"A Mayr","year":"2018","unstructured":"Mayr, A. et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9, 5441\u20135451 (2018).","journal-title":"Chem. Sci."},{"key":"594_CR9","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1146\/annurev-pharmtox-010919-023324","volume":"60","author":"H Zhu","year":"2020","unstructured":"Zhu, H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 60, 573\u2013589 (2020).","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"594_CR10","doi-asserted-by":"crossref","unstructured":"Borhani, D. W. & Shaw, D. E. The future of molecular dynamics simulations in drug discovery. J. Comput. Aided Mol. Des. 26, 15\u201326 (2012).","DOI":"10.1007\/s10822-011-9517-y"},{"key":"594_CR11","unstructured":"Modulus discovery closes $20.4M USD Series C. Modulus (1 March 2022)."},{"key":"594_CR12","unstructured":"Jarvis, L. M. Relay Therapeutics launches to tackle protein movement. Chemical & Engineering News (14 September 2016)."},{"key":"594_CR13","unstructured":"Bell, J. Gilead taps into machine learning to find new NASH drugs. BioPharma Dive (16 April 2019)."},{"key":"594_CR14","doi-asserted-by":"publisher","first-page":"e9942","DOI":"10.15252\/msb.20209942","volume":"17","author":"EJ Chory","year":"2021","unstructured":"Chory, E. J., Gretton, D. W., DeBenedictis, E. A. & Esvelt, K. M. Enabling high-throughput biology with flexible open-source automation. Mol. Syst. Biol. 17, e9942 (2021).","journal-title":"Mol. Syst. Biol."},{"key":"594_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.tips.2020.11.009","volume":"42","author":"C Ma","year":"2021","unstructured":"Ma, C., Peng, Y., Li, H. & Chen, W. Organ-on-a-chip: a new paradigm for drug development. Trends Pharmacol. Sci. 42, 119\u2013133 (2021).","journal-title":"Trends Pharmacol. Sci."},{"key":"594_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.ddtec.2020.06.001","volume":"32\/33","author":"D Reker","year":"2019","unstructured":"Reker, D. Practical considerations for active machine learning in drug discovery. Drug Discov. Today Technol. 32\/33, 73\u201379 (2019).","journal-title":"Drug Discov. Today Technol."},{"key":"594_CR17","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1126\/science.adg6276","volume":"379","author":"M Wadman","year":"2023","unstructured":"Wadman, M. FDA no longer needs to require animal tests before human drug trials. Science 379, 127\u2013128 (2023).","journal-title":"Science"},{"key":"594_CR18","doi-asserted-by":"publisher","first-page":"e1475","DOI":"10.1002\/wcms.1475","volume":"10","author":"J Hemmerich","year":"2020","unstructured":"Hemmerich, J. & Ecker, G. F. In silico toxicology: from structure\u2013activity relationships towards deep learning and adverse outcome pathways. Wiley Interdiscip. Rev. Comput. Mol. Sci. 10, e1475 (2020).","journal-title":"Wiley Interdiscip. Rev. Comput. Mol. Sci."},{"key":"594_CR19","doi-asserted-by":"publisher","first-page":"572","DOI":"10.2174\/1381612822666151125000550","volume":"22","author":"MH Baig","year":"2016","unstructured":"Baig, M. H. et al. Computer aided drug design: successes and limitations. Curr. Pharm. Des. 22, 572\u2013581 (2016).","journal-title":"Curr. Pharm. Des."},{"key":"594_CR20","first-page":"479","volume":"91","author":"A Bhardwaj","year":"2011","unstructured":"Bhardwaj, A. et al. Open source drug discovery\u2014a new paradigm of collaborative research in tuberculosis drug development. Tuberculosis 91, 479\u2013486 (2011).","journal-title":"Tuberculosis"},{"key":"594_CR21","unstructured":"Weingarten, M. D. E. Shaw Research licenses first-in-class therapeutic for immunological diseases to Lilly. D. E. Shaw Research (13 June 2022)."},{"key":"594_CR22","unstructured":"Hale, C. Schr\u00f6dinger\u2019s in-house pipeline helps fetch $2.7B molecule discovery deal with BMS. Fierce Biotech (18 January 2020)."},{"key":"594_CR23","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1111\/1540-5885.310005","volume":"3","author":"F-J Olleros","year":"1986","unstructured":"Olleros, F.-J. Emerging industries and the burnout of pioneers. J. Prod. Innov. Manag. 3, 5\u201318 (1986).","journal-title":"J. Prod. Innov. Manag."},{"key":"594_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/0022-1082.25448","volume":"53","author":"M Pagano","year":"2002","unstructured":"Pagano, M., Panetta, F. & Zingales, L. Why do companies go public? An empirical analysis. J. Financ. 53, 27\u201364 (2002).","journal-title":"J. Financ."},{"key":"594_CR25","doi-asserted-by":"crossref","unstructured":"Eboli, M., Ozel, B., Teglio, A. & Toto, A. Connectivity, centralisation and \u2018robustness-yet-fragility\u2019 of interbank networks. Ann. Finance 19, 169\u2013200 (2022).","DOI":"10.1007\/s10436-022-00416-9"},{"key":"594_CR26","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.3390\/biomedicines10092325","volume":"10","author":"CM Alexander","year":"2022","unstructured":"Alexander, C. M. et al. Trends and perspectives of biological drug approvals by the FDA: a review from 2015 to 2021. Biomedicines 10, 2325 (2022).","journal-title":"Biomedicines"},{"key":"594_CR27","unstructured":"Madura Jayatunga, L. B., Ludwig, R., Schulze, U. & Meier, C. in In Vivo (Pharma Intelligence, 2022)."},{"key":"594_CR28","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1146\/annurev-pharmtox-010814-124852","volume":"55","author":"JM Lajoie","year":"2015","unstructured":"Lajoie, J. M. & Shusta, E. V. Targeting receptor-mediated transport for delivery of biologics across the blood\u2013brain barrier. Annu. Rev. Pharmacol. Toxicol. 55, 613\u2013631 (2015).","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"594_CR29","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.drudis.2020.12.009","volume":"26","author":"A Bender","year":"2021","unstructured":"Bender, A. & Cort\u00e9s-Ciriano, I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov. Today 26, 511\u2013524 (2021).","journal-title":"Drug Discov. Today"},{"key":"594_CR30","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1038\/s41587-020-0521-4","volume":"38","author":"M Eisenstein","year":"2020","unstructured":"Eisenstein, M. Active machine learning helps drug hunters tackle biology. Nat. Biotechnol. 38, 512\u2013514 (2020).","journal-title":"Nat. Biotechnol."},{"key":"594_CR31","doi-asserted-by":"crossref","unstructured":"AlphaFold and beyond. Nat. Methods 20, 163 (2023).","DOI":"10.1038\/s41592-023-01790-6"},{"key":"594_CR32","doi-asserted-by":"publisher","unstructured":"Markey, C. & Croset, S. ComputationalDrugRD. Zenodo https:\/\/doi.org\/10.5281\/zenodo.10482006 (2024).","DOI":"10.5281\/zenodo.10482006"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00594-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00594-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00594-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T17:07:19Z","timestamp":1709053639000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00594-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["594"],"URL":"https:\/\/doi.org\/10.1038\/s43588-024-00594-8","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"3 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a scientific mentor for the German Accelerator, and serves on the scientific advisory board of Areteia Therapeutics. S.C., O.R.W., C.M.B., C.K. and D.K. are employees of Bellevue Asset Management and members of the investment team BB Biotech, with investments in the biotechnology industry including some of the companies analyzed here\u2014a full list of the portfolio is available at .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}