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Boniolo, F ; Dorigatti, E. ; Ohnmacht, A. ; Saur, D.* ; Schubert, B. ; Menden, M.P.

Artificial intelligence in early drug discovery enabling precision medicine.

Expert Opin. Drug Discov. 16, 991-1007 (2021)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Artificial Intelligence ; Biomarker Discovery ; Deep Learning ; Drug Repurposing ; Machine Learning ; Patient Stratification ; Precision Medicine ; Protein Design ; Small Molecule Design ; Vaccine Design; Epitope Vaccine Design; Deep Learning Approach; Mhc Class-i; Neural-networks; Lung-cancer; Cell-lines; Big Data; Prediction; Tumor; Generation
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1746-0441
e-ISSN 1746-045X
Zeitschrift Expert Opinion on Drug Discovery
Quellenangaben Band: 16, Heft: 9, Seiten: 991-1007 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
G-503800-001
Förderungen Postdoctoral Fellowship Program of the Helmholtz Zentrum Muenchen
Munich School for Data Science (MuDS, from the Helmholtz Association)
European Research Council (ERC) under the European Union
DOI 10.1080/17460441.2021.1918096
WOS ID WOS:000657250100001
Scopus ID 85107505118
PubMed ID 34075855
Erfassungsdatum 2021-07-06
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