Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

中文翻译：

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使用机器学习和深度学习方法对药物引起的耳毒性进行计算机模拟预测

药物引起的耳毒性已成为一个严重的全球性问题，因为每年导致数十万人耳聋。它总是由接触导致内耳损伤和退化的药物或环境化学物质引起。在此，我们专注于药物诱导的化学品耳毒性的计算机模拟。我们收集了 1,102 种耳毒性药物和 1,705 种非耳毒性药物。基于该数据集，利用在线化学数据库和建模环境上实施的不同传统机器学习和深度学习算法开发了一系列计算模型。六个 ML 模型在 5 倍交叉验证和测试集上表现最佳。共识模型是用最好的个体模型开发的。这些模型通过外部验证进一步验证。共识模型显示出最好的预测能力，在测试集上准确率为 0.95，在验证集上准确率为 0.90。用于模型开发的共识模型和数据集可在 https://ochem.eu/model/46566321 上获得。此外，还确定了 16 个导致药物引起的耳毒性的结构警报。我们希望这些结果可以为药物发现和环境风险评估中的耳毒性评估提供有意义的知识和有用的工具。