DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: 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

Creator: Fralish, Zachary
Chen, Ashley
Skaluba, Paul
Reker, Daniel

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13321-023-00769-x

URN: urn:nbn:de:101:1-2024011513431765726467

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:27 AM CEST

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Fralish, Zachary
Chen, Ashley
Skaluba, Paul
Reker, Daniel
SpringerLink (Online service)

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DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

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