Background: The prediction of the drug-induced liver injury (DILI) of chemicals is still a key issue of the adverse drug reactions (ADRs) that needs to be solved urgently in drug development. The development of a novel method with good predictive capability and strong mechanism interpretation is still a focus topic in exploring the DILI. Objective: With the help of systems biology and network analysis techniques, a class of descriptors that can reflect the influence of drug targets in the pathogenesis of DILI is established. Then a machine learning model with good predictive capability and strong mechanism interpretation is developed between these descriptors and the toxicity of DILI. Methods: After overlapping the DILI disease module and the drug-target network, we developed novel descriptors according to the number of drug genes with different network overlapped distance parameters. The hepatotoxicity of drugs is predicted based on these novel descriptors and the classical molecular descriptors. Then the DILI mechanism interpretations of drugs are carried out with important network topological descriptors in the prediction model. Results: First, we collected targets of drugs and DILI-related genes and developed 5 NT parameters (S, Nds=0, Nds=1, Nds=2, and Nds>2) based on their relationship with a DILI disease module. Then hepatotoxicity predicting models were established between the above NT parameters combined with molecular descriptors and drugs through the machine learning algorithms. We found that the NT parameters had a significant contribution in the model (ACCtraining set=0.71, AUCtraining set=0.76; ACCexternal set=0.79, AUCexternal set=0.83) developed by these descriptors within the applicability domain, especially for Nds=2, and Nds>2. Then, the DILI mechanism of acetaminophen (APAP) and gefitinib are explored based on their risk genes related to ds=2. There are 26 DILI risk genes in the regulation of cell death regulated with two steps by 5 APAP targets, and gefitinib regulated risk gene of CLDN1, EIF2B4, and AMIGO1 with two steps led to DILI which fell in the biological process of response to oxygen-containing compound, indicating that different drugs possibly induced liver injury through regulating different biological functions. Conclusion: A novel method based on network strategies and machine learning algorithms successfully explored the DILI of drugs. The NT parameters had shown advantages in illustrating the DILI mechanism of chemicals according to the relationships between the drug targets and the DILI risk genes in the human interactome. It can provide a novel candidate of molecular descriptors for the predictions of other ADRs or even of the predictions of ADME/T activity.

中文翻译：

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通过机器学习和网络毒理学方法探索药物的肝毒性

背景：化学品药物性肝损伤（DILI）的预测仍然是药物研发中亟待解决的药物不良反应（ADR）的关键问题。开发一种具有良好预测能力和较强机制解释能力的新方法仍然是探索 DILI 的一个热点课题。目的：借助系统生物学和网络分析技术，建立一类能够反映药物靶点在药源性肝损伤发病机制中影响的描述符。然后在这些描述符和 DILI 的毒性之间开发出具有良好预测能力和强大机制解释的机器学习模型。方法：将 DILI 疾病模块与药物靶点网络重叠后，我们根据具有不同网络重叠距离参数的药物基因的数量开发了新的描述符。根据这些新颖的描述符和经典的分子描述符来预测药物的肝毒性。然后利用预测模型中重要的网络拓扑描述符对药物的 DILI 机制进行解释。结果：首先，我们收集了药物靶标和 DILI 相关基因，并根据它们与 DILI 疾病模块的关系开发了 5 个 NT 参数（S、Nds=0、Nds=1、Nds=2 和 Nds>2）。然后通过机器学习算法将上述NT参数结合分子描述符和药物建立肝毒性预测模型。我们发现 NT 参数对模型有显着贡献（ACCtraining set=0.71，训练集AUC=0.76；ACCexternal set=0.79，AUCexternal set=0.83)由这些描述符在适用范围内开发，特别是对于Nds=2和Nds>2。然后，根据对乙酰氨基酚(APAP)和吉非替尼与ds=2相关的风险基因，探讨其DILI机制。5个APAP靶点对细胞死亡的调控有26个DILI危险基因分两步调节，吉非替尼对CLDN1、EIF2B4、AMIGO1这几个危险基因进行两步调节，导致DILI在氧反应的生物过程中下降。含有化合物，表明不同药物可能通过调节不同的生物学功能而引起肝损伤。结论：基于网络策略和机器学习算法的新方法成功地探索了药物的 DILI。NT 参数在根据药物靶标与人类相互作用组中 DILI 风险基因之间的关系来说明化学物质的 DILI 机制方面显示出优势。它可以为其他 ADR 甚至 ADME/T 活性的预测提供新的分子描述符候选。