Artificial Intelligence Tools for Drug Target Discovery Research: Database, Tools, Applications, and Challenges

ABSTRACT The identification of drug targets remains one of the most critical challenges in pharmaceutical research. The rapid progress of artificial intelligence (AI) is significantly advancing this landscape by enabling more efficient and accurate drug‐target interaction prediction. AI‐driven approaches can integrate and analyze massive biomedical datasets, elucidating complex signaling networks and providing systematic insights into drug mechanisms of action. These developments have greatly accelerated virtual screening, binding affinity estimation, and target identification. However, despite these advancements, key challenges persist, such as ensuring the precision of predictions and overcoming the barriers to integrating AI tools with drug target discovery. This review provides a comprehensive overview of recent public databases, advanced computational methods, and user‐friendly AI tools, highlighting both their potential and challenges. It also offers practical guidance for researchers without computational expertise, illustrating how these technologies can be effectively incorporated into current research workflows to advance drug target discovery and ultimately accelerate the development of novel therapeutic drugs.