Park Jong-Hoon, Cho Young-Rae
Division of Software, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, 26493 Gangwon-do Korea.
Division of Digital Healthcare, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, Gangwon-do 26493 Korea.
Health Inf Sci Syst. 2025 Jan 5;13(1):14. doi: 10.1007/s13755-024-00326-2. eCollection 2025 Dec.
Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
We present a novel network-based framework for drug repositioning, named DRAW+, which incorporates noise filtering and feature extraction using graph neural networks and attention mechanisms. The proposed model first constructs a heterogeneous network that integrates the drug-disease association network with the similarity networks of drugs and diseases, which are upgraded through reduced-rank singular value decomposition. Next, a subgraph surrounding the targeted drug-disease node pair is extracted, allowing the model to focus on local structures. Graph neural networks are then applied to extract structural representation, followed by attention walking to capture key features of the subgraph. Finally, a multi-layer perceptron classifies the subgraph as positive or negative, which indicates the presence of the link between the target node pair.
Experimental validation across three benchmark datasets showed that DRAW+ outperformed seven state-of-the-art methods, achieving the highest average AUROC and AUPRC, 0.963 and 0.564, respectively. Moreover, DRAW+ demonstrated its robustness by achieving the best performance across two additional datasets, further confirming its generalizability and effectiveness in diverse settings.
The proposed network-based computational approach, DRAW+, demonstrates exceptional accuracy and robustness, confirming its effectiveness in drug repositioning tasks.
药物重新定位是一种将已批准药物用于新治疗应用的策略，它为传统药物发现提供了一种更快且更具成本效益的替代方法。许多计算方法都采用了基于网络的模型，尤其是那些使用图神经网络来预测药物 - 疾病关联的方法。然而，这些技术常常忽略输入网络的质量，而这是实现准确预测的关键因素。
我们提出了一种用于药物重新定位的新型基于网络的框架，名为DRAW +，它结合了使用图神经网络和注意力机制的噪声过滤和特征提取。所提出的模型首先构建一个异构网络，该网络将药物 - 疾病关联网络与药物和疾病的相似性网络整合在一起，通过降秩奇异值分解对其进行升级。接下来，提取围绕目标药物 - 疾病节点对的子图，使模型能够专注于局部结构。然后应用图神经网络提取结构表示，随后通过注意力游走捕获子图的关键特征。最后，多层感知器将子图分类为正或负，这表明目标节点对之间存在链接。
在三个基准数据集上的实验验证表明，DRAW +优于七种先进方法，分别实现了最高的平均AUROC和AUPRC，分别为0.963和0.564。此外，DRAW +通过在另外两个数据集上取得最佳性能证明了其稳健性，进一步证实了其在不同设置下的通用性和有效性。
所提出的基于网络的计算方法DRAW +表现出卓越的准确性和稳健性，证实了其在药物重新定位任务中的有效性。
