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Farnoud, A. ; Ohnmacht, A. ; Meinel, M. ; Menden, M.P.

Can artificial intelligence accelerate preclinical drug discovery and precision medicine?

Expert Opin. Drug Discov. 17, 661-665 (2022)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Precision medicine leverages molecular biomarkers for selecting optimal treatment strategies. Accordingly, stratifying patients into responder and non-responder has strongly accelerated drug discovery and drug approvals in the last two decades. Recently, the applications of artificial intelligence (AI) in healthcare have been promoting these processes, and continue to improve patient care through systematically analysing large-scale molecular data and electronic health records. In particular, preclinical pharmacogenomics data empowered AI to unfold its full potential.Areas covered: Here, we discuss the opportunities of AI in pharmacogenomics, drug discovery and precision medicine. In particular, we shed some light on the advancements in computational biomedicine from statistical, machine learning (ML) to complex deep learning (DL) models.Expert opinion: AI has already strongly impacted drug discovery, and will continue to revolutionise academic research and the pharmaceutical industry. Its algorithms aid the identification of novel treatment options through molecular signatures and thus pave the way for the next generation of precision medicine.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Artificial Intelligence ; Biomarkers ; Deep Learning ; Drug Discovery ; Machine Learning ; Pharmacogenomics ; Precision Medicine
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1746-0441
e-ISSN 1746-045X
Zeitschrift Expert Opinion on Drug Discovery
Quellenangaben Band: 17, Heft: 7, Seiten: 661-665 Artikelnummer: , Supplement: ,
Verlag Informa Healthcare
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-554700-001
DOI 10.1080/17460441.2022.2090540
WOS ID WOS:000813632900001
PubMed ID 35708267
Erfassungsdatum 2022-09-29
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