DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.
DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.
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DOI：
10.1109/access.2020.3024238
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ryan M
中科院分区：
文献类型：
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作者：
Abdel-Basset M;Hawash H;Elhoseny M;Chakrabortty RK;Ryan M
新型冠状病毒肺炎(新冠肺炎)的迅速传播导致全球死亡率急剧上升。尽管做了许多努力，但针对这种新型病毒的有效疫苗的快速开发将需要相当长的时间，并依赖于利用商业上可获得的药物来识别潜在的抑制剂来识别药物与靶点(DT)的相互作用。受此启发，我们提出了一个新的框架，称为DeepH-DTA，用于预测异质药物的DT结合亲和力。提出了一种用于学习化合物分子拓扑信息的异构图注意(HGAT)模型和一种用于对简化的分子输入行输入系统(SMILES)药物数据序列中的空间序列信息进行建模的双向ConvLSTM层。对于蛋白质序列，我们提出了一种压缩激励的密集卷积网络来学习氨基酸序列中的隐藏表示，同时利用先进的嵌入技术对两种输入序列进行编码。DeepH-DTA的性能是通过使用两个公共数据集(Davis和Kiba)进行的针对尖端方法的广泛实验来评估的，这两个公共数据集包括激酶蛋白家族和相关抑制剂的折中样本。DeepH-DTA在Davis和Kiba数据集上的一致性指数最高，分别为0.924和0.927，均方误差分别为0.195和0.111。此外，利用药品数据库中FDA批准的药物，使用DeepH-DTA对药物与SARS-CoV-2氨基酸序列的亲和力进行了预测，结果表明该模型可以预测最近在许多临床研究中批准的一些SARS-CoV-2抑制剂。
The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.