Peng Lihong, Mao Jiale, Huang Guohua, Han Guosheng, Liu Xin, Liao Wen, Tian Geng, Yang Jialiang
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
School of Information Technology and Administration, Hunan University of Finance and Economics, Changsha 410125, China.
J Chem Inf Model. 2025 Feb 10;65(3):1318-1337. doi: 10.1021/acs.jcim.4c02088. Epub 2025 Jan 28.
Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement. To address the above two problems, in this study, we develop a novel end-to-end framework named DO-GMA for potential DTI identification by incorporating epthwise verparameterized convolutional neural network and the ated ultihead ttention mechanism with shared-learned queries and bilinear model concatenation. DO-GMA first designs a depthwise overparameterized convolutional neural network to learn drug representations from their SMILES strings and protein representations from their amino acid sequences. Next, it extracts drug representations from their 2D molecular graphs through a graph convolutional network. Subsequently, it fuses drug and protein features by combining the gated attention mechanism and the multihead attention mechanism with shared-learned queries and bilinear model concatenation. Finally, it takes the fused drug-target features as inputs and builds a multilayer perceptron to classify unlabeled drug-target pairs (DTPs). DO-GMA was benchmarked against six newest DTI prediction methods (CPI-GNN, BACPI, CPGL, DrugBAN, BINDTI, and FOTF-CPI) under four different experimental settings on four DTI data sets (i.e., DrugBank, BioSNAP, C.elegans, and BindingDB). The results show that DO-GMA significantly outperformed the above six methods based on AUC, AUPR, accuracy, F1-score, and MCC. An ablation study, robust statistical analysis, sensitivity analysis of parameters, visualization of the fused features, computational cost analysis, and case analysis further validated the powerful DTI identification performance of DO-GMA. In addition, DO-GMA predicted that two drug-protein pairs (i.e., DB00568 and P06276, and DB09118 and Q9UQD0) could be interacting. DO-GMA is freely available at https://github.com/plhhnu/DO-GMA.
识别潜在的药物-靶点相互作用（DTIs）是药物发现和药物重新利用中的关键步骤。尽管深度学习有效地解读了DTIs，但大多数基于深度学习的方法仅从单一角度表示药物特征。此外，药物和蛋白质特征的融合方法需要进一步完善。为了解决上述两个问题，在本研究中，我们开发了一种名为DO-GMA的新型端到端框架，通过结合深度可分离参数化卷积神经网络和带有共享学习查询的多头注意力机制以及双线性模型拼接来识别潜在的DTIs。DO-GMA首先设计一个深度可分离参数化卷积神经网络，从药物的SMILES字符串中学习药物表示，并从蛋白质的氨基酸序列中学习蛋白质表示。接下来，它通过图卷积网络从药物的二维分子图中提取药物表示。随后，它通过将门控注意力机制和多头注意力机制与共享学习查询以及双线性模型拼接相结合来融合药物和蛋白质特征。最后，它将融合后的药物-靶点特征作为输入，并构建一个多层感知器来对未标记的药物-靶点对（DTPs）进行分类。在四个DTI数据集（即DrugBank、BioSNAP、秀丽隐杆线虫和BindingDB）的四种不同实验设置下，DO-GMA与六种最新的DTI预测方法（CPI-GNN、BACPI、CPGL、DrugBAN、BINDTI和FOTF-CPI）进行了基准测试。结果表明，基于AUC、AUPR、准确率、F1分数和MCC，DO-GMA显著优于上述六种方法。一项消融研究、稳健统计分析、参数敏感性分析、融合特征可视化、计算成本分析和案例分析进一步验证了DO-GMA强大的DTI识别性能。此外，DO-GMA预测两个药物-蛋白质对（即DB00568和P06276，以及DB09118和Q9UQD0）可能相互作用。DO-GMA可在https://github.com/plhhnu/DO-GMA上免费获取。
