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Abstract：
In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Co-operation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported.
摘要：
在目前的工作中,已经开发了定量结构-活性关系（QSAR）模型，用于药物对四种不同水生物种的生态毒性，大型水蚤,Oncorhynchusmykiss和Pimephalespromelas使用遗传算法（GA）进行特征选择，然后根据经济合作与发展组织（OECD）指南采用偏最小二乘回归技术。采用双重交叉验证方法选择合适的模型。只有2D描述符用于捕获化学信息和模型构建，而模型的验证是通过考虑各种严格的内部和外部验证指标进行的.有趣的是,即使不使用任何LogP术语也可以开发模型，这与毒性对亲脂性的通常依赖性相反。然而,当前的手稿提出了使用计算的logP描述符的高度稳健和更具预测性的模型。进行了适用性领域研究，以便为获得的QSAR模型设置预定义的化学适用性区域。并且在制定优先顺序列表时，未将超出该领域的测试化合物用于进一步分析。与ECOSAR进行了额外的比较，用于有机污染物毒性预测的在线专家系统，以证明所获得模型的可预测性。所获得的稳健的共识模型用于预测大约9300个药物样分子的大型数据集的毒性，以便根据缩放技术后的急性预测水生毒性对现有的药物样物质进行优先级排序。最后,报告了通过各自的共识模型获得的500种毒性最大的化学品的优先清单以及通过ECOSAR工具预测的清单。
