Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String
Joint Authors
Przybyłek, Maciej
Jeliński, Tomasz
Cysewski, Piotr
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-10
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors.
In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested.
The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents.
By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding.
Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization.
The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters.
Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.
American Psychological Association (APA)
Przybyłek, Maciej& Jeliński, Tomasz& Cysewski, Piotr. 2019. Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String. Journal of Chemistry،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1172143
Modern Language Association (MLA)
Przybyłek, Maciej…[et al.]. Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String. Journal of Chemistry No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1172143
American Medical Association (AMA)
Przybyłek, Maciej& Jeliński, Tomasz& Cysewski, Piotr. Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String. Journal of Chemistry. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1172143
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172143