Artificial Intelligence-Driven Drug Repurposing for Neurodegenerative Diseases: A Computational Analysis and Prediction Study

Abstract

Neurodegenerative diseases represent a significant global health challenge with limited therapeutic options and high drug development failure rates. This study presents a comprehensive artificial intelligence framework for drug repurposing in neurodegenerative diseases, leveraging machine learning algorithms and network-based analysis to identify promising therapeutic candidates. Our methodology integrates multiple data sources including genomic databases, protein-protein interaction networks, and clinical datasets to develop predictive models for drug-disease associations. We employed advanced computational techniques including graph neural networks, deep learning architectures, and ensemble methods to analyze drug-target interactions and predict repurposing opportunities. The framework was validated through cross-validation techniques and literature mining approaches. Our results identified several high-ranking drug candidates with strong therapeutic potential for Alzheimer's disease, Parkinson's disease, and other neurodegenerative conditions. Performance evaluation demonstrated superior accuracy compared to traditional computational methods, with significant improvements in prediction precision and recall metrics. The clinical relevance assessment revealed actionable insights for therapeutic development, with particular emphasis on blood-brain barrier penetration and safety profiles. This AI-driven approach represents a paradigm shift in neurodegenerative disease drug discovery, offering accelerated timelines and reduced costs compared to conventional pharmaceutical development processes.