Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
Ecole Nationale Supérieure de Chimie de Rennes, Université de Rennes 1, CNRS, UMR 6226, 11 allée de Beaulieu, CS 50837, 35708 Rennes Cedex 7, France.
J Hazard Mater. 2016 Feb 13;303:28-40. doi: 10.1016/j.jhazmat.2015.09.021. Epub 2015 Oct 22.
Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.
定量构效关系 (QSAR) 模型有望在化学品对人类和环境的风险评估中发挥重要作用。在这项研究中，我们开发了一个经过验证的 QSAR 模型，以预测 329 种农药对大鼠的急性口服毒性，因为很少有 QSAR 模型致力于预测大鼠的农药致死剂量 50 (LD50)。该 QSAR 模型基于 17 个分子描述符，具有稳健性、外部预测性和良好的适用性域。使用经过准牛顿反向传播 (BFGS) 算法训练的 17/9/1 人工神经网络模型获得了最佳结果。外部验证集的预测精度通过 Q(2)ext 和均方根误差 (RMS) 来估计，分别为 0.948 和 0.201。98.6%的外部验证集被正确预测，本模型被证明优于以前发表的模型。因此，本研究中开发的模型提供了出色的预测能力，可以用于预测农药的急性口服毒性，特别是对于那些尚未进行测试以及新的农药。
