Modeling the Relative Fluorescence Intensity Ratio of Eu(III) Complex in Different Solvents Based on QSPR Method

An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Background

Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure.

Results

Here, we propose a computational strategy that integrates molecular descriptors and MOA information to better explain the mechanisms underlying biological endpoints of interest. By applying our methodology, we obtained a statistically robust and validated model to predict the binding affinity to human serum albumin. Our model is also able to provide new venues for the interpretation of the chemical-biological interactions.

Conclusion

Our observations suggest that integrated quantitative models of structural and MOA-activity relationships are promising complementary tools in the arsenal of strategies aiming at developing new safe- and useful-by-design compounds.

Electronic supplementary material

The online version of this article (10.1186/s13321-019-0359-2) contains supplementary material, which is available to authorized users.

QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima (lambda(max)) of organic dyes for dye-sensitized solar cells (DSSCs). The entire set of 70 dyes was divided into a training set of 53 dyes and a test set of 17 dyes according to Kennard and Stones algorithm. Three-dimensional (3D) descriptors were calculated to represent the dye molecules. A ten-descriptor model, with a squared correlation coefficient (R(2)) of 0.9543 and a standard error of estimation (s) of 14.7 nm, was produced by using the stepwise multilinear regression analysis (MLRA) on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-one-out cross-validation procedure, randomization tests, and validation through the external test set. All descriptors involved in the model were derived solely from the chemical structure of the dye molecules, which makes the model very useful to estimate the lambda(max) of dyes before they are actually synthesized.

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.