Using theoretical descriptors in quantitative structure activity relationships: Some physicochemical properties

Abstract

The application of computational techniques to biology, chemistry and physics is growing rapidly. Quantitative structure–activity relationships (QSAR) have been used widely to relate biological activities and physicochemical properties to molecular structural features. A difficulty in this approach has been non‐uniformity of parameter sets resulting in the inability to examine contributions across properties and data sets. Linear solvation energy relationships (LSER) developed by Kamlet and Taft successfully utilize a single set of parameters to correlate a wide range of biological, chemical and physical properties. The empirical LSER solvatochromic parameters have been replaced with theoretically determined parameters to permit greater ease in a priori property prediction. These TLSER descriptors have given good correlations and interpretations for some biological activities. This paper discusses the application of these descriptors to six physicochemical properties involving equilibria, kinetics and spectra. The results show good correlation and physical interpretation.

QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses

“…Hy ¼ ð1 þ N Hy Þ Á log 2 ð1 þ N Hy Þ þ nC Á ð (20) where N Hy is the number of hydrophilic groups (AOH, ASH, ANH), nC the number of carbon atoms and nSK the number of atoms (hydrogen excluded). The Hy descriptor reflects the influence of the hydrophilic groups on the K salt values.…”

“…If VIF equals to one, no intercorrelation exists for each descriptor; if VIF maintains within the range 1.0-5.0, the corresponding model is acceptable; if VIF is larger than 10.0, the corresponding model is unstable. [20] ANN: ANN is suitable for modeling nonlinear relationship; the major advantage is that models can be developed without knowing the exact form of analytical function on which the model should be built. The theory of ANN and application in QSPR studies is extensively discussed in many reviews.…”

Quantification of Non-Covalent Interactions on the Basis of the Thermodynamic Hydrogen Bond Parameters

“…We mention the overall H-bond descriptors of Abraham 25 and similar descriptors of Zhang et al, 31 which are based on experimental data, and also theoretical descriptors of Famini and co-workers. [32][33][34][35] In the latter approach H-bond acidity is represented by a covalent term, e a , the magnitude of the difference between the lowest unoccupied molecular orbital (LUMO) of the solute and the highest occupied molecular orbital (HOMO) of water, and an electrostatic term, q + representing the largest positive formal charge on a hydrogen atom in the solute. The H-bond basicity is defined by a covalent term, e b (the difference between the HOMO of the solute and the LUMO of water), and an electrostatic term, q Ϫ (the most negative formal charge in the solute molecule).…”

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

“…Then, the variance inflation factor (VIF) for the polylinearity of the parameters was studied. Hence the parameters with lower VIF (VIF < 10) remain in the equation (Famini et al, 1992). Also the P-value was calculated at significant level of 5%.…”