Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters.

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients.

A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R2), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R2 and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R2) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R2) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R2) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.

A novel approach for assessment of antitrypanosomal activity of sesquiterpene lactones through additive and non-additive molecular structure parameters.

Human African trypanosomiasis (HAT) or sleeping sickness is a protozoan neglected tropical disease, which is the main health worry in more than 20 countries in Africa. A novel approach is presented to predict the antitrypanosomal activity of sesquiterpene lactones (STLs) in terms of biological activity (pIC50). The largest reported data set of pIC50 for Trypanosoma brucei rhodesiense (Tbr) as one form of HAT are used to derive and test the new model. The new model is based on five additive and two non-additive molecular structural parameters in several frameworks where it can be easily applied through a computer code. It is derived and tested based on 125 and 31 experimental data, respectively, with different types of statistical parameters. The high reliability of the novel model is compared with the best available QSAR models, which use "classical" molecular descriptors, and 3D pharmacophore features. The values of R2 (correlation coefficient), root mean squared error (RMSE), and RMSEP (root mean square error of prediction) of the new model are 0.77, 0.38, and 0.35, respectively. Meanwhile, R2, RMSE, and RMSEP of comparative QSAR models based on complex descriptors are in the ranges 0.71-76, 0.46-0.4, and 0.51-0.44, respectively. The predictive results of the novel approach confirm its high simplicity, reliability, precision, accuracy, and goodness-of-fit.

Simple method for assessment of activities of thrombin inhibitors through their molecular structure parameters.

Thrombi (blood clots) form in blood vessels in thromboembolic disorders, which are among the main reasons for death in the world. A novel approach is presented to predict thrombin inhibitory activities ((log(103/Ki) (nM)) of some classes of non-peptidic thrombin inhibitors. The largest reported data set of log(103/Ki) for 260 thrombin inhibitors are used to derive and test the new model where it can be easily applied through a computer code. The new model is derived and tested based on 201 and 59 experimental data, respectively, where its reliability is established by external and internal validations. The reliability of the novel correlation is compared with the complex 3D-QSAR method CoMSIA based on donor hydrogen bond, electrostatic interactions, steric occupancy, local hydrophobicity, and acceptor hydrogen bond fields. The values of correlation coefficient (R2), and root mean squared error (RMSE) for 138/34 data of training/test sets, where the predicted results of complex CoMSIA calculations were available, are 0.9173/0.6010 (R2), and 0.2503/0.4911 (RMSE) as well as 0.8753/0.3888 (R2), and 0.3287/0.6358 (RMSE) for the new and CoMSIA models, respectively. Further statistical parameters also confirm high reliability, precision, accuracy, and the goodness-of-fit of the simple model.

A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method.

A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log ß11) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log ß11 for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ - 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.