The study treated two closer alternative methods of which the principal characteristic: a non-parametric method (the least absolute deviation (LAD)) and a traditional method of diagnosis OLS.This was applied to model, separately, the indices of retention of the same whole of 35 pyrazines (27 pyrazines with 8 other pyrazines in the same unit) eluted to the columns OV-101 and Carbowax-20M, by using theoretical molecular descriptors calculated using the software DRAGON. The detection of influential observations for non-parametric method (LAD) is a problem which has been extensively studied and offers alternative dicapproaches whose main feature is the robustness .here is presented and compared with the standard least squares regression .The comparison between methods LAD and OLS is based on the equation of the hyperplane, in order to confirm the robustness thus to detect by the meaningless statements and the points of lever and validated results in the state approached by the tests statistics: Test of Anderson-Darling, shapiro-wilk, Agostino, Jarque-Bera, graphic test (histogram of frequency) and the confidence interval thanks to the concept of robustness to check if the distribution of the errors is really approximate.

No abstract

EU Directive for the Protection of Laboratory Animals mandates and encourages the use of alternative methods that could substitute, cut down on, and generally improve animal testing. Quantitative structure-activity relationship models (QSAR) as well as in vitro toxicity testing are among the most notable of such. QSARs are defined as computerized mathematical models that can utilize a compound’s (aromatic amine) biological activity—aquatic toxicity—to calculate or provide the experimental descriptors of the chemical structure of this compound. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) are the approaches we use for the aim of predicting aquatic toxicity. The best models for two descriptors are the electrotopological descriptors derived from E-calc, and the partition coefficient derived by the Hyperchem software, applying a genetic algorithm—variable subset selection procedure. The important values of the statistical parameters obtained by the two approaches were as follows: By MLR: R2= 92.18, Q2 = 90.51, Q2ext= 95.26, F=188.5466, S = 0.1995. By ANN were: Q2 = 94.79, RMSE= 0.16, Q2ext= 91.71, RMSEext=0.18.

A structure / lethal dose 50 (pCIC50) relationship was researched for a set of phenols while favoring a hybrid genetic algorithm (GA) / multiple linear regression (MLR) approaches to the structural parameters being computed with (E-calc) which calcula the Kier–Hall Electrotopological state indices (E- state) and Hyperchem software. Among the more than 100 simple models with two explanatory variables acquired, we chose the model with the best values of the prediction parameter (Q2) and the coefficient of determination (R2). The reliability of the proposed model has also been illustrated using various techniques of evaluation: leave-many out, cross-validation, randomization test, and validation by the test set. pCIC50 = - 0.0835 ± (0.07006) +0.112 ± (0.007408 (logkow)2 - 0.116 ± (0.01797) s-CH3 ntot = 81 ; S= 0.3296 log unit ; Q2(%) = 74.26 ; R2 (%)= 79.24 ; F= 118.3193; P=0,000.

Considering the importance of the statistical analysis of regression in modeling based separately on study for Quantitative structure retention indices on Carbowax 20 M (I Cw20M ) and OV-101 columns (I OV-101 ) relationships (QSRR) are determined for 114 pyrazines. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. Least Absolute Deviation regression diagnostics offers alternative dicapproaches whose main feature is the robustness. Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods. With have been applied for modeling separately retention indices of the same set of (89 pyrazines of Training and 25 of Test) eluted on Columns OV-101 and Carbowax-20M, using theoretical molecular descriptors derived from DRAGON Software and validating the results in the state approached graphically by Probability plot of the error and approached tests statistics of Anderson-Darling, in finished by the confidence interval thanks to robustness concept to check if errors distribution is really approximate.