AI-Driven Approaches for Drug Repurposing: Accelerating Drug Discovery with Machine Learning and Deep Learning

Traditional medicate revelation is time-consuming, costly, and frequently unsuccessful, taking over a decade and billions of dollars with tall disappointment rates in clinical trials.To address these challenges, sedate repurposing-finding unused employments for approved drugs-offers a quicker, more cost-effective elective by leveraging existing security information and bypassing early improvement stages.Artificial Insights (AI) has changed medicate repurposing by empowering the investigation of tremendous biomedical datasets to identify promising drug-disease intuitive.Machine Learning (ML), deep Learning (DL), Chart Neural Systems (GNNs), and Natural Dialect Handling (NLP) are key instruments in this domain.These strategies have effectively anticipated modern medicate applications, even amid basic times like the COVID-19 widespread.However, challenges stay, counting information heterogeneity, need of labelled data, restricted interpretability, exploratory approval needs, and regulatory obstacles.This paper surveys AI-driven sedate repurposing methods-covering ML, DL, GNNs, and NLP-alongside real-world applications and case considers.We too highlight current confinements and propose future bearings to improve model reliability, straightforwardness, and clinical pertinence.By investigating the synergy between AI and medicate repurposing, this paper points to guide analysts and partners toward more shrewdly and efficient medicate discovery.