Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity
Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity
复制标题
基于深度学习的药物诱导性心脏毒性预测
DOI：
10.1021/acs.jcim.8b00769
复制
发表时间：
2019-03-01
影响因子：
5.6
通讯作者：
Cheng, Feixiong
中科院分区：
文献类型：
--
作者：
Cai, Chuipu;Guo, Pengfei;Cheng, Feixiong
小分子阻断人类乙醚-a-go-go相关基因（hERG）通道可导致QT间期延长，从而导致致命的心脏毒性，并导致许多已批准药物的停用或严格限制使用。在这项研究中，我们开发了一种深度学习方法，称为deephERG，用于预测药物发现和上市后监测中的小分子hERG阻滞剂。我们总共在hERG上组装了7,889种具有明确实验数据和不同化学结构的化合物。我们发现由多任务深度神经网络（DNN）算法构建的深度神经网络模型优于由单任务深度神经网络、朴素贝叶斯（NB）、支持向量机（SVM）、随机森林（RF）和图卷积神经网络（GCNN）构建的深度神经网络模型。其中，在验证集上，deephERG最佳模型的接收者工作特征曲线（AUC）值下面积为0.967。此外，基于1824种美国食品和药物管理局（FDA）批准的药物，通过deephERG计算确定29.6%的药物具有潜在的hERG抑制活性，突出了hERG风险评估在早期药物发现中的重要性。最后，我们展示了几种经临床病例报告、实验证据和文献验证的经批准的抗肿瘤药物的新型预测hERG阻滞剂。总之，本研究提出了一个强大的基于深度学习的工具，用于药物发现和上市后监测中heg介导的心脏毒性的风险评估。
Blockade of the human ether-a-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, na ve Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.