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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Cardiotoxicity Prediction Based on Integreted hERG Database with Molecular Convolution Model
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Cardiotoxicity Prediction Based on Integreted hERG Database with Molecular Convolution Model

机译：基于分子卷积模型的集成hERG数据库的心脏毒性预测

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Cardiotoxicity caused by drug candidates and chemical compounds that block hERG channels may lead to malignant ventricular arrhythmias and even sudden cardiac death (SCD). Various in-silico models have been built to predict the cardiotoxicity during early stages of drug design. The largest public database of hERG-related compounds by integrating several major databases has been constructed recently, which made it possible to build more sophisticated machine learning models for accurate prediction of cardiotoxicity. Here we developed a novel molecular graph convolution neural network (MGCNN) model, based on the new integrated database. The MGCNN models were built by altering the number of graph convolutional layers (GC) from 1 to 5. A random forest (RF) model input with the extended-connectivity fingerprint (ECFP) of different maximal radii (1 ~ 5) was built to enable a direct comparison with the MCGNN models. We found that the MGCNN model with 2 GCs has the best performance in terms of the ROC-AUC-score (0.84), whereas the RF model input with ECFP has a stable performance (0.77 ~ 0.80) over the preset radii. The machine learning models promise a potential new approach for harnessing the big data to achieve accurate prediction of drug cardiotoxicity.
机译：由候选药物和化合物阻断hERG通道引起的心脏毒性可能导致恶性室性心律失常，甚至导致心源性猝死（SCD）。已经建立了各种计算机模拟模型来预测药物设计早期的心脏毒性。最近，通过整合几个主要数据库，建立了最大的hERG相关化合物公共数据库，这使得建立更复杂的机器学习模型以准确预测心脏毒性成为可能。在此，我们基于新的集成数据库开发了一种新颖的分子图卷积神经网络（MGCNN）模型。通过将图卷积层（GC）的数量从1更改为5来建立MGCNN模型。建立了具有不同最大半径（1〜5）的扩展连通性指纹（ECFP）的随机森林（RF）模型输入，以便可以与MCGNN模型进行直接比较。我们发现，具有2个GC的MGCNN模型在ROC-AUC分数（0.84）方面具有最佳性能，而使用ECFP的RF模型输入在预设半径上具有稳定的性能（0.77〜0.80）。机器学习模型有望利用潜在的新方法来利用大数据来实现对药物心脏毒性的准确预测。

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