Drug–drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.

中文翻译：

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机器学习预测与细胞色素 P450 同工酶相关的代谢药物相互作用

药物-药物相互作用（DDI）经常引起严重的不良反应，从而造成不可估量的经济和社会损失。目前，由于实验评估过程耗时且成本高昂，全面的 DDI 评估已成为药物研究中的一项重大挑战，因此非常有必要开发有效的计算机方法来准确、高效地预测和评估 DDI。本研究基于与 CYP450 5 种重要同工酶（CYP1A2、CYP2C9、CYP2C19、CYP2D6 和 CYP3A4）相关的大量底物和抑制剂，通过两种机器学习方法构建了一系列代谢 DDI 的高性能预测模型。随机森林和 XGBoost）和 4 种不同类型的描述符（MOE_2D、CATS、ECFP4 和 MACCS）。为了减少单个模型的不确定性，共识方法被应用于产生更可靠的预测。一系列评估表明，共识模型对于新药组合的 DDI 预测更加可靠和稳健。对于内部验证，DDI 模型的整体预测精度和 AUC 值分别在 0.8 和 0.9 左右。当应用于外部数据集时，多级验证和外部验证的模型精度分别为 0.793 和 0.795。此外，我们还将我们的模型与一些最近发布的工具进行了比较，然后应用最终模型来预测 FDA 批准的药物，并提出了 54,013 种可能具有潜在 DDI 的药物对。总之，