3 research outputs found
Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String
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