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Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state.

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Title: Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state.
Authors: Gheta, Sadra Kashef Ol1, Bonin, Anne1, Gerlach, Thomas2,3, Göller, Andreas H.1, andreas.goeller@bayer.com
Source: Journal of Computer-Aided Molecular Design; Dec2023, Vol. 37 Issue 12, p765-789, 25p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 173367441
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PubType: Academic Journal
PubTypeId: academicJournal
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10822-023-00538-w
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      – Code: eng
        Text: English
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        PageCount: 25
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      – TitleFull: Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state.
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            NameFull: Gheta, Sadra Kashef Ol
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            NameFull: Bonin, Anne
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            NameFull: Gerlach, Thomas
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            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
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              Value: 0920654X
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              Value: 37
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              Value: 12
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            – TitleFull: Journal of Computer-Aided Molecular Design
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