What are the applications of AI in drug discovery?

Introduction to AI in Drug Discovery

Drug discovery is one of the most complex, expensive, and time‐consuming processes in medical research. With the advent of artificial intelligence (AI), many traditional challenges in this field are being addressed. AI is reshaping the landscape of drug discovery by adding computational power to extract insights from massive datasets, thereby reducing research cycle times, costs, and failure rates. In a general sense, AI in drug discovery involves the use of algorithms and models (ranging from machine learning/deep learning to natural language processing) to predict molecular properties, design novel compounds, repurpose existing drugs, and optimize clinical trial strategies.

Definition and Basic Concepts of AI

AI is broadly defined as the capability of machines to mimic human intelligence, engage in reasoning, and learn from data. In the pharmaceutical field, AI commonly refers to systems that use machine learning (ML), deep learning (DL), and natural language processing (NLP) to process large volumes of chemical, biological, and clinical data. AI systems are