Attention-Guided Multiview Deep Learning Framework Uncovers miRNA-Drug Associations for Therapeutic Discovery

MicroRNAs (miRNAs) play critical roles in regulating various biological processes and offer significant potential for treating human diseases. Aberrant expression of miRNAs is known to contribute to drug resistance/sensitivity, posing a significant challenge to miRNA-based therapeutic approaches. Currently, traditional biological experiments to detect miRNA-drug associations (MDAs) are costly and time-consuming, while sequence- or topology-based deep learning methods have gained recognition for their efficiency and accuracy. Nevertheless, existing computational methods tend to ignore multiple sources of information and are overly reliant on known MDAs. We introduce an attention-guided multiview deep learning framework (DLMVF) for predicting MDAs. Our innovative approach fully integrates multisource information about miRNAs and drugs rather than relying exclusively on interaction graph data. DLMVF contains miRNA attribute view encoder, drug attribute view encoder, and miRNA-drug interactions encoder modules, enabling the extraction of miRNA and drug features from multiple perspectives. Moreover, the DLMVF can enhance the learned latent representations for association prediction through view-level attention, which adaptively learns the importance of different features. To evaluate the effectiveness of DLMVF, we manually constructed an experimental benchmark data set based on the latest database. DLMVF achieves an AUROC of 0.9611 and an AUPRC of 0.9543 on the benchmark data set. Extensive benchmarking demonstrates that the DLMVF outperforms existing methods with good robustness and generalization. In addition, a case study of three common anticancer drugs demonstrates its effectiveness in discovering novel MDAs. Data and source code will be published at https://github.com/Lgubig/DLMVF_model.