He Fang, Duan Lian, Xing Guodong, Chang Xiaojing, Zhou Huixia, Yu Mengnan
Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China.
Department of Child Growth and Development Clinic, The Seventh Medical Center of PLA General Hospital, Beijing, China.
Front Pharmacol. 2025 Apr 28;16:1543966. doi: 10.3389/fphar.2025.1543966. eCollection 2025.
Drug development is a complex and lengthy process, and drug-disease association prediction aims to significantly improve research efficiency and success rates by precisely identifying potential associations. However, existing methods for drug-disease association prediction still face limitations in feature representation, feature integration, and generalization capabilities.
To address these challenges, we propose a novel model named AMFGNN (Adaptive Multi-View Fusion Graph Neural Network). This model leverages an adaptive graph neural network and a graph attention network to extract drug features and disease features, respectively. These features are then used as the initial representations of nodes in the drug-disease association network to enable efficient information fusion. Additionally, the model incorporates a contrastive learning mechanism, which enhances the similarity and differentiation between drugs and diseases through cross-view contrastive learning, thereby improving the accuracy of association prediction. Furthermore, a Kolmogorov-Arnold network is employed to perform weighted fusion of various final features, optimizing prediction performance.
AMFGNN demonstrates a significant advantage in predictive performance, achieving an average AUC value of 0.9453, which reflects the model's high accuracy in prediction.
Cross-validation results across multiple datasets indicate that AMFGNN outperforms seven advanced drug-disease association prediction methods. Additionally, case studies on Hepatoblastoma, asthma and Alzheimer's disease further confirm the model's effectiveness and potential value in real-world applications.
药物研发是一个复杂且漫长的过程，药物-疾病关联预测旨在通过精确识别潜在关联来显著提高研究效率和成功率。然而，现有的药物-疾病关联预测方法在特征表示、特征整合和泛化能力方面仍面临局限性。
为应对这些挑战，我们提出了一种名为AMFGNN（自适应多视图融合图神经网络）的新型模型。该模型利用自适应图神经网络和图注意力网络分别提取药物特征和疾病特征。然后，这些特征被用作药物-疾病关联网络中节点的初始表示，以实现高效的信息融合。此外，该模型还引入了一种对比学习机制，通过跨视图对比学习增强药物和疾病之间的相似性和差异性，从而提高关联预测的准确性。此外，还采用了柯尔莫哥洛夫-阿诺德网络对各种最终特征进行加权融合，优化预测性能。
AMFGNN在预测性能方面表现出显著优势，平均AUC值达到0.9453，这反映了该模型在预测方面的高精度。
多个数据集的交叉验证结果表明，AMFGNN优于七种先进的药物-疾病关联预测方法。此外，对肝母细胞瘤、哮喘和阿尔茨海默病的案例研究进一步证实了该模型在实际应用中的有效性和潜在价值。
