Safety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88–0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81–0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.

中文翻译：

---

InterDILI：通过排列特征重要性和注意机制对药物性肝损伤进行可解释的预测

安全性是制约临床药品上市的重要因素之一。药物性肝损伤（DILI）是药物副作用产生安全问题的主要原因。因此，应评估已批准药物和潜在候选药物的 DILI 风险。目前，体内和体外方法用于测试 DILI 风险，但这两种方法都是劳动密集型、耗时且昂贵的。为了克服这些问题，人们提出了许多用于 DILI 预测的计算机方法。以往的研究表明，DILI预测模型可以作为预筛选工具，并且取得了良好的效果。然而，解释预测结果仍然存在局限性。因此，本研究的重点是解释模型预测，以分析哪些特征可能导致 DILI。为此，收集了五个公开可用的数据集来训练和测试模型。然后，应用各种机器学习方法，使用子结构和物理化学描述符作为输入，并将 DILI 标签作为输出。通过识别以下一般到特定的模式来分析特征重要性的解释：（i）识别总体 DILI 预测的一般重要特征，以及（ii）突出显示与每种化合物的 DILI 预测高度相关的特定分子子结构。结果表明，该模型不仅捕获了先前已知的与 DILI 相关的特性，而且还提出了新的 DILI 物理化学特性的潜在子结构。DILI 预测模型的接收者操作特性下面积 (AUROC) 为 0.88–0.97，精确回忆曲线下面积 (AUPRC) 为 0.81–0.95。由此，我们希望所提出的模型能够帮助早期识别候选药物的潜在 DILI 风险，并为药物开发提供有价值的见解。