Artificial intelligence, machine learning, and drug repurposing in cancer

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http://hdl.handle.net/10138/334562

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Tanoli , Z , Vähä-Koskela , M & Aittokallio , T 2021 , ' Artificial intelligence, machine learning, and drug repurposing in cancer ' , Expert opinion on drug discovery , vol. 16 , no. 9 , pp. 977-989 . https://doi.org/10.1080/17460441.2021.1883585

Titel: Artificial intelligence, machine learning, and drug repurposing in cancer
Författare: Tanoli, Ziaurrehman; Vähä-Koskela, Markus; Aittokallio, Tero
Upphovmannens organisation: Institute for Molecular Medicine Finland
Helsinki Institute of Life Science HiLIFE
University of Helsinki
Medicum
Helsinki Institute for Information Technology
Tero Aittokallio / Principal Investigator
Bioinformatics
Datum: 2021-09-02
Språk: eng
Sidantal: 13
Tillhör serie: Expert opinion on drug discovery
ISSN: 1746-0441
DOI: https://doi.org/10.1080/17460441.2021.1883585
Permanenta länken (URI): http://hdl.handle.net/10138/334562
Abstrakt: Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
Subject: Drug repurposing
precision oncology
machine learning
artificial intelligence
target repositioning
113 Computer and information sciences
Referentgranskad: Ja
Licens: cc_by
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion


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