Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints.
Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints.
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DOI：
10.1186/s12859-017-1638-4
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发表时间：
2017-05-31
影响因子：
3
通讯作者：
Nam H
中科院分区：
文献类型：
--
作者：
Kim E;Nam H
药物性肝损伤（DILI）是药物开发中的一个关键问题，因为DILI会导致临床试验失败和批准的药物从市场上撤回。已经有许多尝试基于肝毒性化合物的体内和计算机模拟鉴定来预测DILI的风险。在本研究中，我们提出了使用加权分子指纹预测DILI的计算机预测模型。在这项研究中，我们使用了881位的分子指纹，并用作描述化合物的每个子结构的存在或不存在的特征。然后，计算并标记每个子结构的贝叶斯概率（DILI阳性或阴性），并根据DILI阳性与DILI阴性概率值的比值确定加权指纹。利用加权指纹特征，采用随机森林（RF）和支持向量机（SVM）算法对预测模型进行训练和评估。交叉验证结果表明，所建模型的准确度分别为73.8%和72.6%，AUC分别为0.791和0.768。在独立测试中，RF和SVM模型的准确率分别为60.1%和61.1%。结果验证了加权特征有助于提高预测模型的整体性能。构建的模型进一步应用于预测草药中的天然化合物以识别DILI潜力，并且使用SVM模型预测13，996种独特的草药化合物为DILI阳性。与非加权模型相比，具有加权特征的预测模型提高了性能。此外，我们预测的最佳性能模型中的药物的DILI的潜力，和预测结果表明，许多中药化合物可能有潜力的DILI。因此，我们可以推断，在没有详细参考相关途径的情况下服用天然产品可能是危险的。考虑到天然草药中化合物的使用频率及其在药物开发中的应用增加，DILI标记将非常重要。本文的在线版本（doi：10.1186/s12859-017-1638-4）包含补充材料，可供授权用户使用。
Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints. In this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model. The prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important. The online version of this article (doi:10.1186/s12859-017-1638-4) contains supplementary material, which is available to authorized users.