Quantitative Structure–Activity Relationship Study on Fish Toxicity of Substituted Benzenes

Abstract

Many chemicals cause latent harm, such as erratic diseases and change of climate, and therefore it is necessary to evaluate environmentally safe levels of dangerous chemicals. Quantitative Structure -Toxicity Relationship (QSTR) analysis has become an indispensable tool in ecotoxicological risk assessments. Our paper used QSTR to deal with the modeling of the acute toxicity of 92 substituted benzenes. The molecular descriptors representing the structural features of the compounds were calculated by CODESSA program. Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were utilized to construct the linear and the nonlinear QSTR models, respectively. The predictive results were in agreement with the experimental values. The optimal QSTR model which was established based on RBFNNs gave a correlation coefficient (R 2 ) of 0.893, 0.876, 0.889 and Root-Mean-Square Error (RMSE) of 0.220, 0.205, 0.218 for the training set, the test set, and the whole set, respectively. RBFNNs proved to be a very good method to assess acute aquatic toxicity of these compounds, and more importantly, the RBFNNs model established in this paper has fewer descriptors and better results than other models reported in previous literatures. The current model allows a more transparent chemical interpretation of the acute toxicity in terms of intermolecular interactions.

Quantitative structure–activity relationship study on the inhibitors of fatty acid amide hydrolase

“…To estimate the predictive ability of the QSAR model, the following parameters were used: (a) square correlation coefficient $R^2 $ , (b) coefficients of determination ($R_0^2 $ and $R_0^{'2} $ ) and (c) slopes $k$ and $k'$ of regression lines through the origin 35, 36. It can be concluded that a QSAR model has an acceptable predictive capability if the following conditions are satisfied 34: …”

Toward a Comprehensive Molecular Design Framework for Reduced Hazard

“…Sophisticated statistical methods, such as neural and fuzzy-neural networks, are also increasingly being applied to correlating toxicity directly to a diverse set of molecular descriptors and facilitating effective modeling of toxicity . Numerous other useful QSAR models have been developed for compounds grouped mostly by functional properties, such as substituted benzenes, carboxylic acids,and alcohols, among others. In addition, extensive computerized databases of QSAR data are available that can be used both as lateral validation of existing QSARs and also to derive new QSAR models for the derivation of heuristic rules that describe toxicity in terms of physicochemical descriptors.…”

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

“…Internal validation parameters (Q 2 , r 2 m(LOO) [67] and rmsep int [68]), external validation parameters (Q 2 ext(F1) , Q 2 ext(F2) [69,70] and Q 2 ext(F3) [71], r 2 m(test) [72] and rmsep ext [68]) and overall validation parameter [r 2 m(overall) ] [70] were also reported. The r 2 m metrics have been recently introduced by the present authors' group [67,72] and extensively used by them [73][74][75][76][77] and also other research groups [78][79][80][81][82]. We have also performed process randomization test for the genetic models [83] and reported the c R 2 p values [84].…”