Artificial intelligence, machine learning, and drug repurposing in cancer

Show simple item record Tanoli, Ziaurrehman Vähä-Koskela, Markus Aittokallio, Tero 2021-09-23T11:56:02Z 2021-09-23T11:56:02Z 2021-09-02
dc.identifier.citation 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 .
dc.identifier.other PURE: 161412537
dc.identifier.other PURE UUID: 900e385c-47c4-4079-afab-0f51a1e475cb
dc.identifier.other WOS: 000617586600001
dc.identifier.other ORCID: /0000-0002-0886-9769/work/100453672
dc.description.abstract 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. en
dc.format.extent 13
dc.language.iso eng
dc.relation.ispartof Expert opinion on drug discovery
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Drug repurposing
dc.subject precision oncology
dc.subject machine learning
dc.subject artificial intelligence
dc.subject target repositioning
dc.subject 113 Computer and information sciences
dc.title Artificial intelligence, machine learning, and drug repurposing in cancer en
dc.type Review Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization Helsinki Institute of Life Science HiLIFE
dc.contributor.organization University of Helsinki
dc.contributor.organization Medicum
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Tero Aittokallio / Principal Investigator
dc.contributor.organization Bioinformatics
dc.description.reviewstatus Peer reviewed
dc.relation.issn 1746-0441
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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