A Trust-Embedded Learning Architecture for Discovering Alternative Drug Indications with Verifiable Computational Integrity

Abstract

Drug repurposing has emerged as an effective strategy in modern healthcare, enabling researchers to discover new therapeutic uses for existing drugs while significantly reducing development time and cost. Traditional drug discovery methods rely heavily on manual laboratory experiments, expert analysis, and prolonged clinical trials, making the process slow, expensive, and limited in scalability. These approaches struggle to handle complex and high-dimensional biomedical data, leading to delayed insights and reduced efficiency. With the rapid growth of healthcare data, there is an increasing need for intelligent and automated systems that can efficiently analyze drug characteristics and predict alternative therapeutic applications. Additionally, conventional systems often lack transparency and strong security mechanisms, making clinical data vulnerable to tampering and reducing trust in research outcomes. To address these challenges, the proposed framework integrates Machine Learning (ML), Deep Learning (DL), and Blockchain technologies to develop a secure and intelligent drug repurposing system. The framework employs Random Forest (RF) as a baseline model and a Two-Dimensional Convolutional Neural Network (CNN2D) as an advanced model to improve prediction accuracy. The CNN2D effectively captures complex feature patterns in structured drug data, enabling precise identification of potential new disease treatments. Furthermore, Web3-based Blockchain technology ensures secure storage of user data, clinical interactions, and experimental records by providing immutability, transparency, and data integrity. By combining Artificial Intelligence (AI)-driven analytics with Blockchain-based security, the system enhances prediction performance, automates decision-making, and ensures reliable data management, offering a scalable and efficient solution for accelerating drug discovery and supporting healthcare innovation.