AI-POWERED DRUG REPURPOSING: A NOVEL APPROACH TO ACCELERATE DRUG DEVELOPMENT
TL;DR
Although AI has revolutionary potential to improve drug development and develop patient-specific treatments, its successful application in healthcare depends on resolving issues like data quality, algorithm interpretability, and the complexities of clinical translation.
Abstract
The conventional drug development pipeline is expensive, time-consuming, and prone to failure. Artificial intelligence (AI) aids in drug repurposing by allowing for the prompt discovery of previously unknown therapeutic uses for approved drugs. This manuscript examines AI approaches used in drug repurposing, including machine learning, deep learning, and multi-omics data integration. We demonstrate how these methods enable tailored treatment, speed up virtual screening, and reveal hidden drug-target correlations. AI overcomes the drawbacks of conventional methods by evaluating extensive biological and clinical datasets; this is demonstrated by its crucial role in the quick identification of COVID-19 therapies. Finally, integrating AI results into clinical practice presents a unique set of challenges. It needs interdisciplinary cooperation and a deep comprehension of regulatory frameworks to close the gap between cutting-edge computational methods and practical healthcare applications. To successfully include AI into drug development processes, it is crucial to make sure that findings derived from AI are both scientifically solid and applicable in clinical settings. In conclusion, although AI has revolutionary potential to improve drug development and develop patient-specific treatments, its successful application in healthcare depends on resolving issues like data quality, algorithm interpretability, and the complexities of clinical translation.