IMPACT OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL RESEARCH AND DRUG DISCOVERY

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in pharmaceutical research and drug discovery, revolutionizing traditional methods and significantly enhancing the efficiency of the entire drug development process. AI-powered tools, including machine learning (ML), deep learning (DL), and natural language processing (NLP), are reshaping key stages of drug discovery, from target identification and drug design to preclinical testing and clinical trials. By leveraging vast datasets and predictive analytics, AI accelerates the discovery of novel therapeutic candidates, reduces costs, and improves success rates. Moreover, AI plays a crucial role in optimizing drug properties, personalizing treatments, and addressing complex challenges such as patient stratification and toxicity prediction. Despite these advancements, the integration of AI into pharmaceutical practices presents challenges, including data quality, model interpretability, and regulatory concerns. This paper explores the impact of AI on pharmaceutical research, highlighting both its potential benefits and limitations, while offering insights into future trends and the evolving role of AI in drug development. Keywords: Artificial Intelligence (AI), Pharmaceutical Research, Drug Discovery, Machine Learning (ML), Deep Learning (DL), Drug Target Identification, Drug Design.