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Characterizing emerging companies in computational drug development.

Markey, C ; Croset, S ; et al.
In: Nature computational science, Jg. 4 (2024-02-01), Heft 2, S. 96-103
academicJournal

Titel:
Characterizing emerging companies in computational drug development.
Autor/in / Beteiligte Person: Markey, C ; Croset, S ; Woolley, OR ; Buldun, CM ; Koch, C ; Koller, D ; Reker, D
Zeitschrift: Nature computational science, Jg. 4 (2024-02-01), Heft 2, S. 96-103
Veröffentlichung: [New York, N.Y.] : Springer Nature, [2021]-, 2024
Medientyp: academicJournal
ISSN: 2662-8457 (electronic)
DOI: 10.1038/s43588-024-00594-8
Schlagwort:
  • Drug Development
  • Drug Industry
  • Algorithms
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article; Review
  • Language: English
  • [Nat Comput Sci] 2024 Feb; Vol. 4 (2), pp. 96-103. <i>Date of Electronic Publication: </i>2024 Feb 26.
  • MeSH Terms: Drug Industry* ; Algorithms* ; Drug Development
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Des. 22, 572–581 (2016). (PMID: 10.2174/138161282266615112500055026601966) ; Bhardwaj, A. et al. Open source drug discovery—a new paradigm of collaborative research in tuberculosis drug development. Tuberculosis 91, 479–486 (2011). (PMID: 21782516) ; Weingarten, M. D. E. Shaw Research licenses first-in-class therapeutic for immunological diseases to Lilly. D. E. Shaw Research (13 June 2022). ; Hale, C. Schrödinger’s in-house pipeline helps fetch $2.7B molecule discovery deal with BMS. Fierce Biotech (18 January 2020). ; Olleros, F.-J. Emerging industries and the burnout of pioneers. J. Prod. Innov. Manag. 3, 5–18 (1986). (PMID: 10.1111/1540-5885.310005) ; Pagano, M., Panetta, F. & Zingales, L. Why do companies go public? An empirical analysis. J. Financ. 53, 27–64 (2002). (PMID: 10.1111/0022-1082.25448) ; Eboli, M., Ozel, B., Teglio, A. & Toto, A. Connectivity, centralisation and ‘robustness-yet-fragility’ of interbank networks. Ann. Finance 19, 169–200 (2022). ; 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). (PMID: 10.3390/biomedicines10092325) ; Madura Jayatunga, L. B., Ludwig, R., Schulze, U. & Meier, C. in In Vivo (Pharma Intelligence, 2022). ; Lajoie, J. M. & Shusta, E. V. Targeting receptor-mediated transport for delivery of biologics across the blood–brain barrier. Annu. Rev. Pharmacol. Toxicol. 55, 613–631 (2015). (PMID: 10.1146/annurev-pharmtox-010814-12485225340933) ; Bender, A. & Cortés-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, 511–524 (2021). (PMID: 10.1016/j.drudis.2020.12.00933346134) ; Eisenstein, M. Active machine learning helps drug hunters tackle biology. Nat. Biotechnol. 38, 512–514 (2020). (PMID: 10.1038/s41587-020-0521-432393920) ; AlphaFold and beyond. Nat. 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  • Entry Date(s): Date Created: 20240227 Date Completed: 20240229 Latest Revision: 20240607
  • Update Code: 20240607

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