Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules

Journal of Chemical Information and Modeling

Volumn 54, Issue 3, 2014, Pages 844-856

  McDonagh, James L ^a,b   Nath, Neetika ^a,b   De Ferrari, Luna ^a,b   Van Mourik, Tanja ^b   Mitchell, John B O ^a,b  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b UNIVERSITY OF ST ANDREWS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; FREE ENERGY; MACHINE LEARNING; MEAN SQUARE ERROR; MOLECULES; SOLUBILITY;

10-FOLD CROSS-VALIDATION; CHEMICAL INFORMATION; MACHINE LEARNING MODELS; ROOT MEAN SQUARED ERRORS; SOLVATION FREE ENERGIES; STRUCTURE PROPERTY RELATIONSHIPS; THEORETICAL CALCULATIONS; THERMODYNAMIC VALUES;

DRUG DELIVERY;

DRUG; WATER;

ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; CRYSTALLIZATION; SOLUBILITY; THERMODYNAMICS;

ARTIFICIAL INTELLIGENCE; CRYSTALLIZATION; MODELS, CHEMICAL; MODELS, MOLECULAR; PHARMACEUTICAL PREPARATIONS; SOLUBILITY; THERMODYNAMICS; WATER;

EID: 84896995296     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci4005805     Document Type: Article

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