Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

dc.contributor.author	Poso, Antti
dc.date.accessioned	2024-04-18T10:47:16Z
dc.date.available	2024-04-18T10:47:16Z
dc.date.issued	2023
dc.identifier.issn	1549-9596
dc.identifier.uri	http://hdl.handle.net/10900/152888
dc.language.iso	en	en
dc.publisher	Washington : Amer Chemical Soc	de_DE
dc.relation.uri	http://dx.doi.org/10.1021/acs.jcim.3c01239
dc.subject.ddc	540	de_DE
dc.subject.ddc	004	de_DE
dc.title	Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries	de_DE
dc.type	Article	de_DE
utue.quellen.id	20240124000000_01422
utue.publikation.seiten	5773-5783	de_DE
utue.personen.roh	Sivula, Toni
utue.personen.roh	Yetukuri, Laxman
utue.personen.roh	Kalliokoski, Tuomo
utue.personen.roh	Kasnanen, Heikki
utue.personen.roh	Poso, Antti
utue.personen.roh	Pohner, Ina
dcterms.isPartOf.ZSTitelID	Journal of Chemical Information and Modeling	de_DE
dcterms.isPartOf.ZS-Issue	18	de_DE
dcterms.isPartOf.ZS-Volume	63	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät
utue.fakultaet	Sonstige