Data-Driven Drug Discovery with Multi-Target Prediction Models

Abstract

This work presents a secure and intelligent approach to drug repurposing by combining machine learning with blockchain technology. The system is designed as a webbased platform where users can register, log in, and interact with drug-related datasets. These datasets are processed and analyzed using different machine learning models to identify possible alternative uses of existing drugs. The system compares multiple models and selects the one that performs best in terms of prediction accuracy. In addition to prediction, blockchain is used to store user activities and shared information in a secure and tamper-proof manner. Users can also contribute trial data and view discussions from others, which supports collaborative research. By integrating artificial intelligence with blockchain, the system improves reliability, transparency, and efficiency in drug discovery, while also reducing the time and cost involved in traditional methods.