Estimation of Hansen solubility parameters using multivariate nonlinear QSPR modeling with COSMO screening charge density moments

New QSPR multivariate nonlinear models based on artificial neural network (ANN) were developed for the prediction of the components of the three-dimensional Hansen solubility parameters (HSPs) using COSMO-RS sigma-moments as molecular descriptors. The sigma-moments are obtained from high quality quantum chemical calculations using the continuum solvation model COSMO and a subsequent statistical decomposition of the resulting polarization charge densities. The models for HSPs were built on a training/validation data set of 128 compounds having a broad diversity of chemical characters (alkanes, alkenes, aromatics, haloalkanes, nitroalkanes, amines, amides, alcohols, ketones, ethers, esters, acids, ion-pairs: amine/acid associates, ionic liquids). The prediction power of the correlation equation models for HSPs was validated on a test set of 17 compounds with various functional groups and polarity, among them drug-like molecules, organic salts, solvents and ion-pairs. It was established that COSMO sigma-moments can be used as excellent independent variables in nonlinear structure–property relationships using ANN approaches. The resulting optimal multivariate nonlinear QSPR models involve the five basic sigma-moments having defined physical meaning and possess superior predictive ability for dispersion, polar and hydrogen bonding HSPs components, with test set correlation coefficients R2d = 0.85, R2p = 0.91, R2h = 0.92 and mean absolute errors of Δδd = 1.37 MPa1/2, Δδp = 1.85 MPa1/2 and Δδh = 2.58 MPa1/2.

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• Nonlinear QSPR models were developed for prediction of Hansen solubility parameters.
• Estimations were done with artificial neural networks.
• COSMO-RS sigma-moments were used as molecular descriptors.