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A computational framework for predicting novel drug indications using graph convolutional network with contrastive learning

计算机科学 机器学习 人工智能 图形 图论 卷积神经网络 自然语言处理 理论计算机科学 数学 组合数学
作者
Yuxun Luo,Wenyu Shan,Peng Li,Lingyun Luo,Pingjian Ding,Wei Liang
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (8): 4503-4511 被引量:2
标识
DOI:10.1109/jbhi.2024.3387937
摘要

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin. The prediction model's code is available at https://github.com/yuxunluo9/DrIGCL .
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