We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP "All Literature" evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.

中文翻译：

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机器学习模型，用于预测环境化学物质的内分泌破坏潜能。

我们在这里介绍ML4Tox，这是一个提供深度学习和支持向量机模型的框架，用于预测化合物的激动剂，拮抗剂和结合活性，在这种情况下为雌激素受体配体结合域。ML4Tox模型已在CERAPP ToxCast数据集的训练部分上以10×5倍交叉验证方案进行开发，该数据由1677种化学物质组成，每种由777个分子特征描述。在CERAPP“所有文献”评估集中（激动剂：6319种化合物；拮抗剂6539；结合7283），与激动剂：0.78 vs 0.56；激动剂相比，ML4Tox显着提高了所有三个任务公布结果的敏感性。拮抗剂：0.69比0.11；绑定：0.66和0.26。