AI-driven Drug Repurposing for Novel Therapeutic Applications
Utilizes AI algorithms to identify existing drugs with potential therapeutic applications in new disease areas
Keywords:
AI, drug repurposing, therapeutic applications, machine learning, deep learning, natural language processing, ethical considerations, regulatory challengesAbstract
The process of drug discovery and development is time-consuming, expensive, and often fails to yield new therapeutic options. Drug repurposing, the identification of new therapeutic uses for existing drugs, offers a faster and more cost-effective approach to bring new treatments to market. Artificial intelligence (AI) algorithms have revolutionized drug repurposing by enabling the rapid analysis of vast amounts of biological and clinical data to identify potential candidates. This paper explores the use of AI-driven drug repurposing for novel therapeutic applications, highlighting its advantages, challenges, and future prospects. Through a comprehensive review of the current literature, we discuss the various AI techniques used in drug repurposing, including machine learning, deep learning, and natural language processing. We also examine the ethical considerations and regulatory challenges associated with AI-driven drug repurposing. Finally, we present case studies that demonstrate the success of AI in identifying new therapeutic uses for existing drugs, showcasing its potential to transform the field of drug discovery and development.
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