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dc.contributor.author
Moret, Michael
dc.contributor.author
Friedrich, Lukas
dc.contributor.author
Grisoni, Francesca
dc.contributor.author
Merk, Daniel
dc.contributor.author
Schneider, Gisbert
dc.date.accessioned
2020-01-10T11:12:37Z
dc.date.available
2020-01-06T13:42:25Z
dc.date.available
2020-01-10T11:12:37Z
dc.date.issued
2019-11-07
dc.identifier.other
10.26434/chemrxiv.10119299.v1
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/387725
dc.identifier.doi
10.3929/ethz-b-000387725
dc.description.abstract
Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities 'on demand'. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knwoledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
American Chemical Societ
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Generating customized compound libraries for drug discovery with machine intelligence
en_US
dc.type
Working Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
CHemRxiv
ethz.pages.start
10119299/1
en_US
ethz.size
27 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Washington, DC
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
en_US
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
en_US
ethz.date.deposited
2020-01-06T13:42:33Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-10T11:12:47Z
ethz.rosetta.lastUpdated
2020-02-15T23:12:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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