Artificial Intelligence-Driven Pharmaceutical Research: A Comprehensive Analysis of Applications and Challenges

Amira Hassan Abed

doi:10.56427/jcbd.v4i1.634

Authors

Amira Hassan Abed Business Information Systems Department, Faculty Of Business Administration, Al Ryada University for Science And Technology

DOI:

https://doi.org/10.56427/jcbd.v4i1.634

Keywords:

Artificial Intelligence (AI), Pharmaceutical Development, Machine Learning (ML), Drug Discovery, Personalized Medicine

Abstract

This review investigates the integration of Artificial Intelligence (AI) in pharmaceutical product development, focusing on its applications in drug discovery, design, manufacturing, and quality control. Key AI methodologies, such as machine learning (ML) and deep learning (DL), are analyzed for their contributions to critical stages, including target identification, molecular screening, and clinical trial optimization. The findings highlight AI's capacity to streamline workflows, reduce development costs, and enhance efficacy, with notable improvements in drug discovery speed, prediction accuracy of drug safety and efficacy, and novel approaches in drug repurposing and personalized medicine. Despite these advancements, challenges such as fragmented data integration, limited availability of specialized skillsets, and resistance to AI adoption remain significant barriers. This review emphasizes the need for industry-wide collaboration to address these issues and leverage AI's full potential. In conclusion, AI demonstrates transformative capabilities in accelerating drug development cycles and enabling precision-driven innovations, promising a paradigm shift in pharmaceutical practices through the convergence of computational power and biological sciences.

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