Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies for this task obviously have great benefits in reducing the drug search space. Most of computational methods to predict DTI are proposed to solve a binary classification problem, which ignore the influence of binding strength. Therefore, drug-target binding affinity prediction is still a challenging issue. Currently, lots of studies only extract sequence information that lacks feature-rich representation, but we consider more spatial features in order to merge various data in drug and target spaces. In this study, we propose a two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, via various information analysis modules. First stage is to utilize sequence and structure information to generate fusion feature map of candidate protein and drug pair through various analysis modules based deep learning. Second stage is to apply bagging-based ensemble learning strategy for regression prediction, and we obtain outstanding results by combining the advantages of various algorithms in efficient feature abstraction and regression calculation. Importantly, we evaluate our novel method, DeepFusionDTA, which delivers 1.5 percent CI increase on KIBA dataset and 1.0 percent increase on Davis dataset, by comparing with existing prediction tools, DeepDTA. Furthermore, the ideas we have offered can be applied to in-silico screening of the interaction space, to provide novel DTIs which can be experimentally pursued. The codes and data are available from https://github.com/guofei-tju/DeepFusionDTA .

中文翻译：

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DeepFusionDTA：利用信息融合和混合深度学习集成模型进行药物靶点结合亲和力预测

药物-靶标相互作用（DTI）的鉴定是药物发现广泛领域中最重要的问题。使用纯生物学实验来验证药物与靶点的结合谱需要花费大量的时间和精力，因此这项任务的计算技术显然在减少药物搜索空间方面有很大的好处。大多数预测 DTI 的计算方法都是为了解决二元分类问题而提出的，它们忽略了结合强度的影响。因此，药物与靶点的结合亲和力预测仍然是一个具有挑战性的问题。目前，许多研究仅提取缺乏特征丰富表示的序列信息，但我们考虑更多的空间特征以合并药物和目标空间中的各种数据。在这项研究中，我们提出了一个两阶段的深度神经网络集成模型，用于通过各种信息分析模块检测药物与靶点的结合亲和力，称为 DeepFusionDTA。第一阶段是利用序列和结构信息，通过基于深度学习的各种分析模块，生成候选蛋白和药物对的融合特征图。第二阶段是应用基于bagging的集成学习策略进行回归预测，结合各种算法在高效特征抽象和回归计算方面的优势，取得了优异的成绩。重要的是，我们评估了我们的新方法 DeepFusionDTA，通过与现有预测工具 DeepDTA 进行比较，该方法在 KIBA 数据集上提高了 1.5% 的 CI，在 Davis 数据集上提高了 1.0%。此外，我们提供的想法可以应用于交互空间的计算机筛选，以提供可以通过实验进行的新型 DTI。代码和数据可从https://github.com/guofei-tju/DeepFusionDTA .