Exploring New Drug Uses through Hybrid Learning and BlockchainSupported Data Validation

Abstract

Drug repurposing has gained significant attention as an efficient strategy for identifying new therapeutic applications of existing drugs, thereby reducing both development time and cost compared to traditional drug discovery processes. Current drug discovery approaches rely on experimental procedures, expert analysis, and extensive clinical trials, which are time-intensive and computationally inefficient when handling large-scale biomedical data. These methods often struggle to process complex and highdimensional datasets, resulting in slower analysis and limited predictive capability. Additionally, these systems lack robust mechanisms for secure data management, making clinical records and trial discussions susceptible to inconsistencies and unauthorized modifications. To overcome these limitations, this work proposes an intelligent drug repurposing framework that integrates Machine Learning (ML), Deep Learning (DL), and blockchain technologies. The system utilizes baseline models such as K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) for comparative analysis, along with a hybrid DrugNet model that combines Convolutional Neural Networks (CNN) for feature extraction and Random Forest (RF) for classification. This hybrid approach enhances the ability to capture complex patterns in drug-related data and improves prediction accuracy. Furthermore, blockchain integration using Web3 ensures secure storage of user data, clinical interactions, and trial information, providing transparency, immutability, and data integrity. The proposed framework enables automated prediction of potential drug–disease associations through a unified processing pipeline, supporting real-time analysis and decision-making. By combining advanced Artificial Intelligence (AI) techniques with decentralized data management, the system improves scalability, reliability, and efficiency in drug repurposing. This approach offers a practical and secure solution for accelerating pharmaceutical research and supporting data-driven medical innovation