Molecular similarity-based predictions of the Tox21 screening outcome
Molecular similarity-based predictions of the Tox21 screening outcome
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DOI：
10.3389/fenvs.2015.00054
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发表时间：
2015-01-01
影响因子：
4.6
通讯作者：
Dunkel, Mathias
中科院分区：
文献类型：
--
作者：
Drwal, Malgorzata N.;Siramshetty, Vishal B.;Dunkel, Mathias
为了评估新化学品和药物的毒性，监管机构要求对许多毒性终点进行体内测试，导致每年进行数百万次动物实验。然而，遵循替换、减少、优化（3R）原则，替代方法的开发和优化，特别是计算机模拟方法，近年来已成为焦点。人们普遍认为，毒性终点越复杂，建模就越困难。因此，计算毒理学正在从建模一般和复杂的终点转向毒性途径和潜在分子效应的研究和建模。美国世纪毒理学（Tox21）计划已经筛选了一个大型化合物库，包括大约10，000种环境化学品和药物，用于引起毒性作用的不同机制，并将结果公开。通过Tox21 Data Challenge，该联盟为计算毒理学家建立了一个平台，以开发和验证他们的预测模型。在这里，我们提出了一种快速而成功的方法，用于预测2014年Tox21数据挑战中核受体和应激反应途径筛选的不同结果。该方法是基于分子相似性计算和朴素贝叶斯机器学习算法的组合，并已实现为KNIME管道。分子被表示为由具有拓扑化合物性质的常见二维分子指纹类型的级联组成的二元向量。预测方法已针对每个建模目标进行了单独优化，并在交叉验证以及独立Tox21验证集中进行了评估。我们的研究结果表明，该方法可以实现良好的预测精度和排名的顶级算法提交的预测挑战，表明其在毒性预测的广泛适用性。
To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modeling general and complex endpoints to the investigation and modeling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the twenty-first century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naive Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modeled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.