Volume-1 ,Issue-2, September-2025

Drug discovery is a time-consuming, costly, and complex process with high attrition rates at multiple stages. Artificial intelligence (AI) has emerged as a transformative tool in modern drug design, offering the ability to analyze large chemical, biological, and clinical datasets, predict molecular interactions, and optimize compounds in silico. This review systematically examines the role, applications, tools, and impact of AI in drug discovery, focusing on machine learning, deep learning, natural language processing, and reinforcement learning. Key applications include target identification and validation, lead compound discovery, de novo drug design, drug repurposing, ADMET prediction, and clinical trial optimization. AI has demonstrated significant potential in reducing time and cost, improving predictive accuracy, and accelerating the development of novel therapeutics. The review also highlights AI-driven software platforms, challenges such as data quality and model interpretability, and emerging trends including integration with omics, personalized medicine, explainable AI, and fully automated drug design. Overall, AI is reshaping pharmaceutical research, offering unprecedented opportunities to enhance efficiency, innovation, and precision in drug development.

Keyword: Artificial Intelligence; Drug Discovery; Machine Learning; Deep Learning; Drug Design; Drug Repurposing; ADMET Prediction; Personalized Medicine.