Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Bioinformatics. 2022 Sep 2;38(17):4153-4161. doi: 10.1093/bioinformatics/btac485.
Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consuming and expensive. With the collection of more validated biomedical data and the advancement of computing technology, the computational methods based on chemogenomics gradually attract more attention, which guide the experimental verifications.
In this study, we propose an end-to-end deep learning-based method named IIFDTI to predict drug-target interactions (DTIs) based on independent features of drug-target pairs and interactive features of their substructures. First, the interactive features of substructures between drugs and targets are extracted by the bidirectional encoder-decoder architecture. The independent features of drugs and targets are extracted by the graph neural networks and convolutional neural networks, respectively. Then, all extracted features are fused and inputted into fully connected dense layers in downstream tasks for predicting DTIs. IIFDTI takes into account the independent features of drugs/targets and simulates the interactive features of the substructures from the biological perspective. Multiple experiments show that IIFDTI outperforms the state-of-the-art methods in terms of the area under the receiver operating characteristics curve (AUC), the area under the precision-recall curve (AUPR), precision, and recall on benchmark datasets. In addition, the mapped visualizations of attention weights indicate that IIFDTI has learned the biological knowledge insights, and two case studies illustrate the capabilities of IIFDTI in practical applications.
The data and codes underlying this article are available in Github at https://github.com/czjczj/IIFDTI.
Supplementary data are available at Bioinformatics online.
鉴定药物-靶标相互作用是药物发现和设计的关键步骤。传统的生化实验可准确验证药物-靶标相互作用，是可信的，但它们也极其费力、耗时且昂贵。随着更多验证的生物医学数据的收集和计算技术的进步，基于化学生物组学的计算方法逐渐受到更多关注，这些方法指导着实验验证。
在这项研究中，我们提出了一种端到端的深度学习方法 IIFDTI，用于基于药物-靶标对的独立特征和它们的子结构的交互特征预测药物-靶标相互作用（DTIs）。首先，通过双向编码器-解码器结构提取药物和靶标之间的子结构的交互特征。然后，通过图神经网络和卷积神经网络分别提取药物和靶标的独立特征。最后，将所有提取的特征融合并输入下游任务的全连接密集层，以预测 DTIs。IIFDTI 考虑了药物/靶标的独立特征，并从生物学角度模拟了子结构的交互特征。多项实验表明，在基准数据集上，IIFDTI 在接收者操作特征曲线下的面积（AUC）、精度-召回率曲线下的面积（AUPR）、精度和召回率方面均优于最先进的方法。此外，注意力权重的映射可视化表明，IIFDTI 已经学习到了生物学知识见解，并且两个案例研究说明了 IIFDTI 在实际应用中的能力。
本文的基础数据和代码可在 Github 上获得，网址为 https://github.com/czjczj/IIFDTI。
补充数据可在生物信息学在线获得。
