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dc.contributor.author
Bruns, Dominique
dc.contributor.author
Merk, Daniel
dc.contributor.author
Kumar, Karthiga S.
dc.contributor.author
Baumgartner, Martin
dc.contributor.author
Schneider, Gisbert
dc.date.accessioned
2019-11-08T07:34:04Z
dc.date.available
2019-11-08T03:43:21Z
dc.date.available
2019-11-08T07:34:04Z
dc.date.issued
2019-10
dc.identifier.other
10.1002/open.201900222
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/376111
dc.identifier.doi
10.3929/ethz-b-000376111
dc.description.abstract
Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
chemoinformatics
en_US
dc.subject
chemotaxis
en_US
dc.subject
drug discovery
en_US
dc.subject
neural networks
en_US
dc.subject
phenotypic screening
en_US
dc.title
Synthetic Activators of Cell Migration Designed by Constructive Machine Learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2019-10-23
ethz.journal.title
ChemistryOpen
ethz.journal.volume
8
en_US
ethz.journal.issue
10
en_US
ethz.pages.start
1303
en_US
ethz.pages.end
1308
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Computational design of custom-tailored chemokine receptor blockers
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Hoboken, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02534 - Institut für Pharmazeutische Wiss. / Institute of Pharmaceutical Sciences::03852 - Schneider, Gisbert / Schneider, Gisbert
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02534 - Institut für Pharmazeutische Wiss. / Institute of Pharmaceutical Sciences::03852 - Schneider, Gisbert / Schneider, Gisbert
ethz.grant.agreementno
159737
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Interdisziplinäres Projekt
ethz.date.deposited
2019-11-08T03:43:32Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-02-15T22:23:04Z
ethz.rosetta.lastUpdated
2021-02-15T06:37:31Z
ethz.rosetta.versionExported
true
ethz.COinS
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