Respiratory toxicity is considered as main cause of drug withdrawal, which could seriously injure human health or even lead to death. The objective of this investigation was to develop an in silico prediction model of drug-induced respiratory toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to respiratory toxicity, and the ECFP_6 fingerprint descriptors were applied to the respiratory toxic/non-toxic fragments production. The established prediction model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier generated overall prediction accuracy of 91.8% for the training set and 84.3% for the external test set. Furthermore, six molecular descriptors (e.g., number of O atoms, number of N atoms, molecular weight, Apol, number of H acceptors and molecular polar surface area) considered as important for the drug-induced respiratory toxicity were identified, and some critical fragments related to the respiratory toxicity were achieved. We hope the established naïve Bayes prediction model could be used as a toxicological screening of chemicals for respiratory sensitization potential in drug development, and these obtained important information of respiratory toxic chemical structures could offer theoretical guidance for hit and lead optimization.

呼吸道毒性被认为是戒断药物的主要原因，这可能会严重损害人体健康甚至导致死亡。这项研究的目的是通过使用朴素的贝叶斯分类器来开发药物诱发的呼吸道毒性的计算机模拟预测模型。遗传算法用于选择与呼吸道毒性有关的重要分子描述符，并将ECFP_6指纹图谱应用于呼吸道有毒/无毒片段的生产。建立的预测模型通过内部5倍交叉验证和外部测试集进行验证。朴素的贝叶斯分类器对训练集产生了91.8％的整体预测准确度，对于外部测试集产生了84.3％的整体预测准确度。此外，六个分子描述符（例如确定了对药物引起的呼吸道毒性很重要的O原子数，N原子数，分子量，Apol，H受体数和分子极性表面积，并确定了一些与呼吸道毒性有关的关键片段实现。我们希望所建立的朴素贝叶斯预测模型可以用作对药物开发中呼吸道致敏潜力的化学物质进行毒理学筛选，并且这些获得的呼吸道有毒化学结构的重要信息可以为命中和铅优化提供理论指导。