Gananathan Kavipriya, Manjula D, Sugumaran Vijayan
School of Computer Science Engineering (SCOPE), Vellore Institute of Technology, Chennai, 600127, India.
Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI, 48309, USA.
BMC Bioinformatics. 2025 Jul 20;26(1):185. doi: 10.1186/s12859-025-06141-0.
Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) techniques become essential. The prediction of DTI is a challenging process due to the absence of known drug-target relationship and no experimentally verified negative samples. The datasets with limited or unbalanced data, do not perform well. The models that use heterogeneous networks, non-linear fusion techniques, and heuristic similarity selection may need a lot of computational power and experience to implement and fine-tune. The latest developments in machine learning (ML) and deep learning (DL) models can be employed for effective DTI prediction process.
To that end, this study develops a novel DTI Prediction model, namely, DTIP-WINDGRU Drug-Target Interaction Prediction with Wind-Enhanced GRU. The major aim is to determine the DTIs in both labelled and unlabelled samples accurately compared to traditional wet lab experiments. To accomplish this, the proposed DTIP-WINDGRU model primarily performs pre-processing and class labelling. In addition, drug-to-drug (D-D) and target-to-target (T-T) interactions are employed to initialize the weights of the GRU model and are employed for the, DTI prediction process. Finally, the Wind Driven Optimization (WDO) algorithm is utilized to optimally choose the hyperparameters involved in the GRU model.
For ensuring the effectual prediction results of the DTIP-WINDGRU model, a widespread experimentation process was carried out using four datasets. This comprehensive comparative study highlighted the better performance of the DTIP-WINDGRU model over existing techniques.
药物靶点相互作用（DTI）的识别是药物发现过程的重要组成部分。由于使用实验室测试预测DTI既耗时又费力，因此使用计算智能（CI）技术的自动化工具变得至关重要。由于缺乏已知的药物-靶点关系且没有经过实验验证的阴性样本，DTI的预测是一个具有挑战性的过程。数据有限或不平衡的数据集表现不佳。使用异构网络、非线性融合技术和启发式相似性选择的模型可能需要大量的计算能力和经验来实现和微调。机器学习（ML）和深度学习（DL）模型的最新发展可用于有效的DTI预测过程。
为此，本研究开发了一种新颖的DTI预测模型，即DTIP-WINDGRU（基于风增强门控循环单元的药物-靶点相互作用预测模型）。主要目的是与传统的湿实验室实验相比，准确确定标记和未标记样本中的DTI。为了实现这一目标，所提出的DTIP-WINDGRU模型主要进行预处理和类别标记。此外，药物-药物（D-D）和靶点-靶点（T-T）相互作用用于初始化门控循环单元模型的权重，并用于DTI预测过程。最后，利用风驱动优化（WDO）算法来优化选择门控循环单元模型中涉及的超参数。
为了确保DTIP-WINDGRU模型取得有效的预测结果，使用四个数据集进行了广泛的实验过程。这项全面的比较研究突出了DTIP-WINDGRU模型相对于现有技术的更好性能。
