Quantitative Structure鈥揂ctivity Relationship Models for Ready Biodegradability of Chemicals

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• 作者：Kamel Mansouri ; Tine Ringsted ; Davide Ballabio ; Roberto Todeschini ; Viviana Consonni
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：April 22, 2013
• 年：2013
• 卷：53
• 期：4
• 页码：867-878
• 全文大小：405K
• 年卷期：v.53,no.4(April 22, 2013)
• ISSN：1549-960X

文摘

The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure鈥揳ctivity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.