SMCL-MDR: Predicting miRNA-Drug Resistance using Self-Attention Mechanism and Contrastive Learning

To date, the study of the resistance between miRNA (microRNA) and drugs has consistently garnered widespread attention.Understanding the resistance between miRNAs and drugs holds significant implications for disease treatment and the development of new drugs. However, traditional biological experimental models are time-consuming and costly, and existing computational models still need further improvement in capturing complex miRNA drug resistance. We propose a miRNA-drug resistance prediction model based on a self-attention mechanism and contrastive learning(SMCL-MDR). The model uses the self-attention mechanism to encode the features of miRNAs and drugs, adaptively capturing their complex resistance. The self-attention mechanism effectively handles long-distance dependencies, enhancing the model’s focus on important features. In addition, to enhance the model’s robustness and prediction accuracy, we employ a contrastive learning strategy. By creating positive and negative sample pairs, the model maximizes the similarity among positive pairs and minimizes it among negative pairs, thereby improving the learning of feature representations for miRNAs and drugs. Experimental results show that our model attains AUC and AUPR scores of 0.9896 and 0.9881 on the ncDR dataset, significantly outperforming existing calculation models and demonstrating substantial application potential. The proposed miRNA-drug resistance prediction model based on the self-attention mechanism and contrastive learning not only exhibits high prediction accuracy and robustness but also provides new ideas and methods for future miRNA-drug research.