Quantum mechanical and QSAR study of some α-arylpropionic acids as anti-inflammatory agents

A model of atomic compressibility and its application in QSAR domain for toxicological property prediction

A model for computing the atomic compressibility (β) based on two periodic descriptors, namely, absolute radius (r) and atomic electrophilicity index (ω), is proposed as[Formula: see text]The ansatz is invoked to compute compressibilities of atoms of 57 elements of the periodic table. The computed atomic data exhibits all sine qua non of periodic properties. Further, the concept group compressibility (Gβ) is also established invoking additivity property using some molecules with different functional groups and consequently utilized in correlating with molecular polarizability. Since toxicity prediction is an imperative need of the hour, chemical reactivity descriptors are of paramount importance in the study of toxicological behaviour along with a lot of other molecular reactivity studies within a Quantitative Structure-Activity Relationship (QSAR) context. Hence, this quantity is applied in the modelling of toxicological property through QSAR and a comprehensive study is performed in an effort to investigate and validate the application of compressibility in determining its toxicological power. Consequently, varied 209 organic molecules are selected for studying the toxic effect on Tetrahymena pyriformis. A QSAR model is constructed in terms of compressibility which offers a superior prediction of toxicity independently without adopting additional descriptors or properties as in some other QSAR studies. Graphical abstract.

The conformational behaviour of naproxen and flurbiprofen in solution by NMR spectroscopy

The conformational equilibrium of common anti-inflammatory drugs has been studied experimentally in solution by NMR in weakly ordered PBLG phases.

Quantum chemical calculations to reveal the relationship between the chemical structure and the fluorescence characteristics of phenylquinolinylethynes and phenylisoquinolinylethynes derivatives, and to predict their relative fluorescence intensity

In this paper the relationship between the chemical structure and fluorescence characteristics of 30 phenylquinolinylethyne (PhQE), and phenylisoquinolinylethyne (PhIE) derivatives compounds employing ab initio calculations have been elucidated. Quantum chemical calculations (6-31G) were carried out to obtain: the optimized geometry, energy levels, charges and dipole moments of these compounds, in the singlet (steady and excited states) and triplet states. The relationship between quantum chemical descriptors, and wavelength of maximum excitation and emission indicated that these two parameters have the most correlation with quantum chemical hardness (eta). Also, stokes shift has the most correlation with the square of difference between the maximum of positive charges in the singlet steady and singlet excited states. The quantitative structure-property relationship (QSPR) of PhQE and PhIE was studied for relative fluorescence intensity (RFI). The genetic algorithm (GA) was applied to select the variables that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) and support vector machine (SVM) were both utilized to construct linear and non-linear QSPR models, respectively. The SVM model demonstrated a better performance than that of the MLR model. The route mean square error (RMSE) in the training and the test sets for the SVM model was 0.195 and 0.324, and the correlation coefficients were 0.965 and 0.960, respectively, thus revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for QSPR studies. According to the best of our knowledge, this is the first research on QSPR studies to predict RFI for a series of PhQE and PhIE derivative compounds using SVM.

QSAR study of p56(lck) protein tyrosine kinase inhibitory activity of flavonoid derivatives using MLR and GA-PLS

Quantitative relationships between molecular structure and p56(lck) protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R(2) = 0.74 and Q(2) = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56(lck) protein tyrosine kinase inhibitors than those provided previously.

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.

Electrophilicity as a possible descriptor for toxicity prediction

Electrophilicity is one of the cardinal chemical reactivity descriptors successfully employed in various molecular reactivity studies within a structure-activity relationship parlance. The applications of this quantity in the modeling of toxicological properties have inspired us to perform a more exhaustive study in order to test and/or to validate the application of electrophilicity in assessing its chemical and toxicological potential. For this reason the toxicity of a large data set of molecules comprising 252 aliphatic compounds on the Tetrahymena pyriformis is studied. A quantitative structure-activity relationship analysis enabled us to model toxicity in terms of global and local electrophilicities, which provide a reasonably good prediction of aliphatic toxicity. It is heartening to note that the global and local electrophilicity values together can explain the toxicity of a large variety of aliphatic compounds nicely without resorting to any other descriptor or other microscopic/macroscopic physicochemical properties as is the situation in all other QSAR studies.

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%.