DeepDTA: deep drug-target binding affinity prediction.
DeepDTA: deep drug-target binding affinity prediction.
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DOI：
10.1093/bioinformatics/bty593
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发表时间：
2018-09-01
期刊：
影响因子：
--
通讯作者：
Ozkirimli E
中科院分区：
文献类型：
--
作者：
Öztürk H;Özgür A;Ozkirimli E
新药-靶点(DT)相互作用的识别是药物发现过程的重要组成部分。大多数已提出的预测DT相互作用的计算方法都集中在二进制分类上，其中的目标是确定DT对是否相互作用。然而，蛋白质-配体的相互作用假设了一系列的结合强度值，也称为结合亲和力，预测这个值仍然是一个挑战。DT知识库中可用亲和力数据的增加允许使用高级学习技术，如深度学习体系结构来预测结合亲和力。在这项研究中，我们提出了一个基于深度学习的模型，该模型只使用靶标和药物的序列信息来预测DT相互作用结合亲和力。很少有人利用蛋白质-配体复合体的三维结构或化合物的二维特征来预测DT结合亲和力。这项工作中使用的一种新方法是用卷积神经网络(CNN)对蛋白质序列和化合物一维表示进行建模。结果表明，利用靶点和药物的一维表示建立的基于深度学习的模型是一种有效的药物靶点亲和力预测方法。通过CNN构建药物和靶点的高级表示的模型在我们的一个较大的基准数据集中获得了最佳的一致性指数(CI)性能，超过了KronRLS算法和用于DT结合亲和力预测的最先进方法SimBoost。Https://github.com/hkmztrk/DeepDTA补充数据可在生物信息学在线上获得。
The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction. https://github.com/hkmztrk/DeepDTA Supplementary data are available at Bioinformatics online.
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