Accelerating Drug Repurposing with AI: A Case Study of Existing Drugs

Abstract

Artificial intelligence (AI) is a revolutionary driver of drug discovery and repurposing, providing new avenues to speed up pharmaceutical innovation and precision medicine. Conventional drug development is time-consuming, expensive, and usually limited by high attrition rates. Current advancements illustrate the way AI-based approaches— spanning machine learning, deep learning, natural language processing, and generative models—facilitate swift discovery of drug candidates for various diseases like COVID-19, Alzheimer's, cancer, multiple sclerosis, and pulmonary hypertension. AI aids in hypothesis generation, structure-based screening, biomarker stratification, and supply chain optimization, and even implements clinical data for real-world validation. Research emphasizes its contribution to the discovery of synergistic drug combinations, adaptive trial design augmentation, and verification of action mechanisms using bio-simulation and large language models. However, despite all these advances, challenges still abound with regard to data quality, model interpretability, ethical adoption, and regulatory buy-in. Nevertheless, AI-assisted repurposing is a cost-efficient avenue to find new uses for approved drugs, thereby shortening development timelines and increasing therapeutic possibilities. Together, these findings highlight AI's potential to transform drug discovery pipelines and shorten the bench-to-bedside gap. Future directions highlight interdisciplinary teaming, explainable AI models, and the incorporation of clinician feedback to optimize translational potential.

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