Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity (⁠|${q}_{\mathrm{test}}^2$| = 0.784). In addition, we get the applicability domain of the models by calculating the standard deviation distance and the lever distance of the training set. We also found that some molecular descriptors are closely related to neurotoxicity by calculating the contribution of the molecular descriptors to the models. Considering the accuracy of the regression models, we recommend using the extra-trees regressor model to predict the chemical autonomic neurotoxicity.

中文翻译：

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使用机器学习对化学神经毒性进行计算机模拟预测。

神经毒性是戒断药物的主要原因之一，而检测神经毒性毒性的生物学实验方法既费时又费力。此外，现有的神经毒性计算预测模型仍存在一些不足。针对这些缺点，我们收集了大量神经毒性数据集，并使用PyBioMed分子描述符和八种机器学习算法构建化学神经毒性回归预测模型。通过模型的交叉验证和测试集验证，发现树外回归模型对神经毒性具有最佳预测效果（⁠| $ {q} _ {\ mathrm {test}} ^ 2 $ |= 0.784）。此外，我们通过计算标准偏差距离和训练集的杠杆距离来获得模型的适用范围。通过计算分子描述符对模型的贡献，我们还发现某些分子描述符与神经毒性密切相关。考虑到回归模型的准确性，我们建议使用树外回归模型来预测化学自主神经毒性。