Aqueous solubility prediction: Do crystal lattice interactions help?

Salahinejad, M, Le, T and Winkler, D 2013, 'Aqueous solubility prediction: Do crystal lattice interactions help?', Molecular Pharmaceutics, vol. 10, no. 7, pp. 2757-2766.

Document type: Journal Article
Collection: Journal Articles

Title Aqueous solubility prediction: Do crystal lattice interactions help?
Author(s) Salahinejad, M
Le, T
Winkler, D
Year 2013
Journal name Molecular Pharmaceutics
Volume number 10
Issue number 7
Start page 2757
End page 2766
Total pages 10
Publisher Molecular Pharmaceutics
Abstract Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly diverse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.
Subject Theory and Design of Materials
Cheminformatics and Quantitative Structure-Activity Relationships
Electroanalytical Chemistry
Keyword(s) aqueous solubility
Bayesian neural network
enthalpy of sublimation
lattice energy
optimally sparse models
VolSurf descriptors
DOI - identifier 10.1021/mp4001958
Copyright notice © 2013 by the American Chemical Society.
ISSN 1543-8384
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