Abstract
MicroRNAs (miRNAs) are important in gene expression regulation and many other biological processes and have emerged as promising therapeutic targets. Identifying potential drug-miRNA relationships is helpful in disease therapy and pharmaceutical engineering in medical research. However, accurately predicting these relationships remains a significant computational challenge. This study introduces MDbDMRP, a novel molecular descriptors-based drug-miRNA relationship prediction computational model designed to address this challenge. MDbDMRP leverages the power of machine learning to predict new drug-miRNA associations and non-associations. The model achieves exceptional performance, exceeding an average score of 0.92 across various evaluation metrics, including accuracy, precision, recall, and F1-score. Furthermore, it demonstrates a remarkable ability to distinguish between positive and negative interactions, as evidenced by an outstanding AUC-ROC score of 0.9864. The results obtained from MDbDMRP were further validated through molecular docking, reinforcing its performance. These results position MDbDMRP as a valuable tool for researchers aiming to unlock the potential of miRNAs in drug discovery.
Published Version
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