Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors.

Feng Luan,M Natália D S Cordeiro,Lili Tang,Ting Wang

doi:10.3390/ijms19113423

Abstract

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

Highlights

It has been widely accepted that the environmental pollutants usually exist and play a role in the form of mixtures, rather than as single chemicals
Researchers have proposed two different approaches to evaluate the toxicity of mixtures, that is, the experimental toxicity tests to experimentally measure the toxicity of a whole mixture [4,5,6], and computational toxicology methods, such as quantitative structure–activity relationship (QSAR) studies, to predict the toxicity of the mixture [7,8,9,10]
Prediction of the toxicity of a sequence of mixture was accomplished by the multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) methods

Summary

Introduction

It has been widely accepted that the environmental pollutants usually exist and play a role in the form of mixtures, rather than as single chemicals. Researchers have proposed two different approaches to evaluate the toxicity of mixtures, that is, the experimental toxicity tests to experimentally measure the toxicity of a whole mixture [4,5,6], and computational toxicology methods, such as quantitative structure–activity relationship (QSAR) studies, to predict the toxicity of the mixture [7,8,9,10]. QSAR, as a computational method, has been used for almost six decades, and was widely applied in physical chemistry, pharmaceutical chemistry, environmental chemistry, toxicology, and other research fields [10]. It has been proven that it can be used to evaluate the properties, activities, and toxicities as effectively as the alternative methods to the experimental methods

Methods

Results

Conclusion