DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
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DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning ; volume:15 ; number:1 ; day:27 ; month:10 ; year:2023 ; pages:1-13 ; date:12.2023
Journal of cheminformatics ; 15, Heft 1 (27.10.2023), 1-13, 12.2023
- Urheber
-
Fralish, Zachary
Chen, Ashley
Skaluba, Paul
Reker, Daniel
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
-
10.1186/s13321-023-00769-x
- URN
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urn:nbn:de:101:1-2024011513431765726467
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:27 MESZ
Datenpartner
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Beteiligte
- Fralish, Zachary
- Chen, Ashley
- Skaluba, Paul
- Reker, Daniel
- SpringerLink (Online service)
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