Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders

The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeutic targets. Traditional methods often struggle to cope with the complexity of heterogeneous networks and the high dimensionality of biological data. In this study, we propose a circRNA-drug association prediction method based on multi-scale convolutional neural networks (MSCNN) and adversarial autoencoders, named AAECDA. First, we construct a feature network by integrating circRNA sequence similarity, drug structure similarity, and known circRNA-drug associations. Then, unlike conventional convolutional neural networks, we employ MSCNN to extract hierarchical features from this integrated network. Subsequently, adversarial characteristics are introduced to further refine these features through an adversarial autoencoder, obtaining low-dimensional representations. Finally, the learned representations are fed into a deep neural network to predict novel circRNA-drug associations. Experiments show that AAECDA outperforms various baseline methods in predicting circRNA-drug associations. Additionally, case studies demonstrate that our model is applicable in practical related tasks.