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dc.contributor.author
Jiménez-Luna, José
dc.contributor.author
Grisoni, Francesca
dc.contributor.author
Weskamp, Nils
dc.contributor.author
Schneider, Gisbert
dc.date.accessioned
2021-09-28T07:16:09Z
dc.date.available
2021-07-15T10:43:30Z
dc.date.available
2021-07-19T13:30:32Z
dc.date.available
2021-09-28T07:16:09Z
dc.date.issued
2021
dc.identifier.issn
1746-0441
dc.identifier.issn
1746-045X
dc.identifier.other
10.1080/17460441.2021.1909567
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/495078
dc.identifier.doi
10.3929/ethz-b-000495078
dc.description.abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry. Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery. Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Taylor & Francis
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Drug discovery
en_US
dc.subject
artificial intelligence
en_US
dc.subject
QSAR
en_US
dc.subject
de novo drug design
en_US
dc.subject
synthesis prediction
en_US
dc.title
Artificial intelligence in drug discovery: recent advances and future perspectives
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2021-04-02
ethz.journal.title
Expert Opinion on Drug Discovery
ethz.journal.volume
16
en_US
ethz.journal.issue
9
en_US
ethz.journal.abbreviated
Expert Opin. Drug Discov.
ethz.pages.start
949
en_US
ethz.pages.end
959
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
De novo molecular design by deep learning
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03852 - Schneider, Gisbert / Schneider, Gisbert
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03852 - Schneider, Gisbert / Schneider, Gisbert
en_US
ethz.grant.agreementno
182176
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.date.deposited
2021-07-15T10:44:36Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-09-28T07:16:16Z
ethz.rosetta.lastUpdated
2022-03-29T13:35:42Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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