Machine Learning Meets Medicine: AI's Role in Drug Discovery and Development

The drug discovery and development process is a complex, high-risk endeavor marked by substantial time, cost, and failure rates, particularly in clinical trials. Recent advancements in artificial intelligence (AI) have introduced promising solutions to overcome these challenges. AI, leveraging techniques like machine learning (ML) and deep learning (DL), has begun to significantly enhance the drug development pipeline by streamlining key stages such as drug target identification, screening of potential compounds, clinical trial design, and drug repurposing. These AI-powered methods enable the analysis of large and diverse datasets to uncover insights and predict outcomes that were previously unattainable. Through AI, researchers can now identify promising drug targets, design novel drug candidates, uncover new biomarkers for disease progression, optimize patient recruitment for clinical trials, and even repurpose existing drugs for new therapeutic indications. While these technologies hold transformative potential, the integration of AI into drug development faces challenges such as the need for high-quality, standardized data, algorithmic transparency, and regulatory acceptance. This review explores the current applications and future prospects of AI in drug discovery, underscoring its role in accelerating the development of effective, personalized treatments while also addressing the obstacles that must be overcome for its full implementation.