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An, Hongle, Liu, Xuyang, Cai, Wensheng, Shao, Xueguang (2024) AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology. Journal of Chemical Information and Modeling, 64 (14) 5480-5491 doi:10.1021/acs.jcim.4c00449

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Reference TypeJournal (article/letter/editorial)
TitleAttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology
JournalJournal of Chemical Information and Modeling
AuthorsAn, HongleAuthor
Liu, XuyangAuthor
Cai, WenshengAuthor
Shao, XueguangAuthor
Year2024 (July 22)Volume64
Page(s)5480-5491Issue14
PublisherAmerican Chemical Society (ACS)
DOIdoi:10.1021/acs.jcim.4c00449Search in ResearchGate
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Mindat Ref. ID17505984Long-form Identifiermindat:1:5:17505984:5
GUID0
Full ReferenceAn, Hongle, Liu, Xuyang, Cai, Wensheng, Shao, Xueguang (2024) AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology. Journal of Chemical Information and Modeling, 64 (14) 5480-5491 doi:10.1021/acs.jcim.4c00449
Plain TextAn, Hongle, Liu, Xuyang, Cai, Wensheng, Shao, Xueguang (2024) AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology. Journal of Chemical Information and Modeling, 64 (14) 5480-5491 doi:10.1021/acs.jcim.4c00449
In(2024, July) Journal of Chemical Information and Modeling Vol. 64 (14) American Chemical Society (ACS)

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