University Côte d'Azur, I3S Laboratory , UMR CNRS 7271, CS 40121, 06903 Sophia Antipolis Cedex, France.
Bayer SAS , 06903 Sophia Antipolis Cedex, France.
J Chem Inf Model. 2019 Apr 22;59(4):1486-1496. doi: 10.1021/acs.jcim.8b00553. Epub 2019 Feb 27.
The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to assess toxicity as early as possible. To enable the development of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capability. For this, a large scale analysis on the entire database has been performed to build models to predict bioactivities measured in in vitro assays. Simple classical QSAR algorithms (ANN, SVM, LDA, random forest, and Bayesian) were first applied on the data, and the results of these algorithms suggested that they do not seem to be well-suited for data sets with a high proportion of inactive compounds. The study then showed for the first time that the use of an ensemble method named "Stacked generalization" could improve the model performance on this type of data. Indeed, for 61% of 483 models, the Stacked method led to models with higher performance. Moreover, the combination of this ensemble method with an applicability domain filter allows one to assess the reliability of the predictions for further compound prioritization. In particular we showed that for 50% of the models, the ROC score is better if we do not consider the compounds that are not within the applicability domain.
开发能够预测化学物质生物活性和毒性的计算工具是一种强大的解决方案，旨在尽早评估毒性。为了能够开发此类工具，ToxCast 计划已经生成并公开了数千种化合物的体外生物活性数据。本研究的目的是从机器学习能力的角度对 ToxCast 的数据进行特征描述和探索。为此，对整个数据库进行了大规模分析，以构建预测体外测定中生物活性的模型。首先将简单的经典 QSAR 算法（人工神经网络、支持向量机、线性判别分析、随机森林和贝叶斯）应用于数据，这些算法的结果表明，它们似乎不太适合具有高比例非活性化合物的数据集。该研究首次表明，使用名为“堆叠泛化”的集成方法可以提高此类数据的模型性能。实际上，在 483 个模型中的 61%中，堆叠方法使模型的性能更高。此外，该集成方法与适用性域过滤器的结合可以评估进一步化合物优先级排序的预测可靠性。特别是我们表明，如果我们不考虑不在适用性域内的化合物，对于 50%的模型，ROC 得分会更好。
