Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure-activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC50) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q2) for training sets and the predictive squared correlation coefficients (Rp2) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.

中文翻译：

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苯胺和苯酚对水生生物毒性预测的简单方法。

环境中的化学污染已引起全世界的关注，化学污染物的毒性预测也变得非常重要。在本文中，我们介绍了一种简单的方法来预测某些化学成分的毒性，该方法采用Tchebichef图像矩（TM）方法从分子结构图像中提取有用的化学信息，从而建立定量构效关系（QSAR） ）预测模型。该方法被用于预测苯胺和苯酚对水生P.subcapitata和V.fischeri的毒性，其中将获得的TMs定义为自变量，而将生物毒性（pEC50）视为自变量。因变量。然后，通过逐步回归建立预测模型，分别。两组数据的留一法交叉验证的平方相关系数（Q2）和测试集的预测平方相关系数（Rp2）分别高于0.79和0.75，这表明所获得的模型具有令人满意的精度和可靠性。与已报道的几种方法相比，该方法更加方便，具有较高的预测能力。我们的研究为QSAR研究提供了另一个视角。这表明所获得的模型具有令人满意的准确性和可靠性。与已报道的几种方法相比，该方法更加方便，具有较高的预测能力。我们的研究为QSAR研究提供了另一个视角。这表明所获得的模型具有令人满意的准确性和可靠性。与已报道的几种方法相比，该方法更加方便，具有较高的预测能力。我们的研究为QSAR研究提供了另一个视角。