Published Paper Details:

Artificial intelligence (AI) has emerged as a transformative tool in modern drug design and drug discovery, enabling faster, more efficient, and cost-effective identification of new therapeutic candidates. AI-driven approaches such as machine learning, deep learning, natural language processing, and generative modelling support critical stages of drug development including target identification, lead optimization, prediction of pharmacokinetic and toxicity profiles, and virtual screening of large chemical libraries. By analyzing complex biological datasets and recognizing patterns beyond human capability, AI significantly accelerates the early discovery process and improves decision-making accuracy. Despite these advancements, challenges remain, including dataset quality issues, algorithmic bias, lack of interpretability, limited biological understanding, and the need for robust experimental validation. This review provides an in-depth analysis of current AI applications in drug discovery, outlines key advantages and limitations, highlights ethical and regulatory concerns, and discusses future directions required for safe and effective integration of AI technologies in pharmaceutical research.