PDF(Pubmed)
Abstract：
Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.
摘要：
由于药物机制的复杂性，很难预测化合物是否会引起药物性肝损伤（DILI）。半胱氨酸捕获测定法是用于检测与微粒体共价结合的反应性代谢物的方法。然而,使用35S同位素标记的半胱氨酸进行该测定是麻烦的。因此,我们构建了一个计算机模拟分类模型,用于预测半胱氨酸捕获试验的阳性/阴性结果.根据本研究的实验数据，我们收集了475种化合物（436种内部化合物和39种公开可用的药物），结果的组成显示248个阳性和227个阴性。使用消息传递神经网络（MPNN）和具有扩展连接指纹（ECFP）4的随机森林（RF），我们建立了机器学习模型来预测化合物的共价结合风险。在时间分割数据集中，保持试验中MPNN和RF的AUC-ROC分别为0.625和0.559，限制性的。该结果表明，在时间分割数据集中，MPNN模型比RF具有更高的预测性。因此，我们得出的结论是，用于半胱氨酸捕获测定的计算机MPNN分类模型具有更好的预测能力。此外，对半胱氨酸捕获测定有积极贡献的大多数亚结构与以前的结果一致.
