Prediction of Solute Descriptors in LSER Equation Using Quantitative Structure-Property Relationship Methodology - FATEMI, M.H.; GHASEMI, M.A. (Asian Journal of Chemistry, 2010)
In this study, a quantitative structure-property relationship method based on multiple linear regressions (MLR) and artificial neural network (ANN) techniques were applied for the calculation/prediction of Σβ2H and π2H parameters of the linear solvation energy relationship (LSER). The selected descriptors that appear in multiple linear regression models for Σβ2H are: maximal electrotopological positive variation, average connectivity index chi-5, Geary autocorrelation-lag1/weighted by atomic polarizabilities, radial distribution function-2/unweighted and leverageweighted autocorrelation-lag 4/unweighted. Also descriptors that appear in MLR model for π2H are: Geary autocorrelation-lag2/weighted by atomic Sanderson electronegativites, 2nd component accessibility directional WHIM index/weighted by atomic vander Waals volumes, d COMMA-2 value/weighted by atomic Sanderson electronegativites, number of H attached to C1(sp3)/C0(sp2) and dipole moment. These descriptors were used as inputs for two ANNs. After training and optimization of these ANNs, they were used to prediction of π2H and Σβ2H values of the test set compounds, separately. Analysis of the results obtained indicates that the models we proposed can correctly represent the relationship between these LSER solute parameters and theoretically calculated molecular descriptors. Also results showed the superiority of neural networks over regression models.