MDCL: Multi-source Information Fusion and Collaborative Contrastive Learning for Inferring miRNA-Drug Sensitivity and...

A growing body of evidence indicates that microRNAs (miRNAs) play essential roles in cancer progression, therapy, and drug resistance. They are increasingly recognized as promising therapeutic targets for overcoming cancer drug resistance. However, existing computational methods for predicting miRNA-drug associations are often limited by sparse labeled associations, reliance on single-modality data, and insufficient ability to capture long-range dependencies in sequences. In this study, we propose a novel deep learning framework, named MDCL, that integrates multi-source information fusion with collaborative contrastive learning. MDCL uses a multi-scale convolutional neural network (multi-scale CNN) to learn sequence embeddings at different granularities from miRNA sequences and drug SMILES strings. In parallel, frequency chaos game representation (FCGR) and multiple molecular fingerprints are used to characterize the two-dimensional structural patterns of miRNAs and the physicochemical properties of drugs, respectively. To effectively integrate multi-view information, the model introduces a cross-view attention mechanism for feature enhancement and constructs a local-global alignment loss within a collaborative contrastive learning (CCL) framework, substantially improving feature robustness and discriminability. We evaluated MDCL on benchmark datasets manually curated from the latest database release. Extensive five-fold cross-validation experiments, repeated five times with different random seeds, show that MDCL consistently outperforms state-of-the-art methods, achieving an AUC of 0.9450 and an AUPR of 0.9281 for miRNA-drug resistance (MDR) association prediction, and an AUC of 0.9379 and an AUPR of 0.9293 for miRNA-drug sensitivity (MDS) association prediction. MDCL also attains the best F1-score and Matthews correlation coefficient (MCC) among all compared methods. Moreover, under a strict independent-node cold-start setting, in which both miRNAs and drugs in the test set are unseen during training, MDCL remains competitive in ranking-based metrics, demonstrating strong generalization ability. In addition, a case study of docetaxel further demonstrates the practical utility of the model for discovering novel miRNA-drug associations. The data and source code are available at https://github.com/scxdhr020629/MDCL .