Quantitative Electronic Structure-Activity Relations: The Influence of Basis Set Selection on Prediction Quality

Application of quantum topological molecular similarity descriptors in QSPR study of the O-methylation of substituted phenols

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2--C1--O--H fragment of the parent molecule. Indeed, the C2--X and C4--X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, lambda1, lambda2, lambda3, epsilon, and K(r) were detected as highly influential parameters.

Accurate prediction of the blood-brain partitioning of a large set of solutes usingab initiocalculations and genetic neural network modeling

A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood-brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMS(V) and RMS(CV) equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data.

Application ofab initio theory to QSAR study of 1,4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures

The usefulness of the quantum chemical descriptors, calculated at the level of the RHF theory using 6-31G basis set for QSAR study of 1,4-dihydropyridine-based calcium channel antagonist was examined. A data set containing 45 dihydropyridine derivatives with known activity was used. Multiple linear regressions combined with genetic algorithm for variable selection and an artificial neural network model combined with principal component analysis for dimension reduction and genetic algorithm for factor selection (PC-GA-ANN) were employed. Some multiparametric MLR equations with good statistical quality were obtained for different classes of dihydropyridine derivatives. The resulting equations suggest that the electronic properties of the atoms belonging to the backbone of the molecules as well as the conformation of the molecules affect the binding of these molecules with their receptor. In the PC-GA-ANN, The principal components of the descriptors data matrix were used as the input of the neural network and then genetic algorithm was applied to select the most relevant set of principal components. Two ANN models with five selected principal components were obtained. These models, which have high statistical qualities, can predict the activity of the molecules with prediction errors lower than +/-5%.