Drug discovery, as the core driving force of the modern pharmaceutical industry, faces the difficulty of the traditional model's "high investment, long cycle, and low output, " urgently requiring breakthroughs to address increasingly complex health demands. The rapid development of artificial intelligence (AI) technology has brought revolutionary changes to drug discovery, significantly enhancing efficiency and success rates in areas such as protein structure prediction, protein design, antibody drug design, and small molecule drug development. This article focuses on the domestic and international progress of AI in these key domains, providing an in-depth analysis of AI breakthrough in protein structure prediction and its potential applications in target discovery and virtual screening. It explores the closed-loop model of AI-driven protein design, from structure prediction to functional innovation, and examines AI's role in antibody sequence optimization, affinity maturation, and novel antibody design. Additionally, it reviews the latest achievements of AI in small molecule drug target identification, virtual screening, and ADMET optimization. The article also highlights challenges in AI applications, including data quality, model interpretability, and experimental validation, while envisioning future directions such as multimodal data integration, dynamic behavior prediction, and automated platforms. By comprehensively analyzing the current state and challenges of AI-enabled drug discovery, this article aims to offer scientific perspectives and insights to accelerate new drug creation and enhance human health and well-being. It seeks to provide readers with a thorough and insightful view of technological issues in AI-empowered drug discovery, stimulate thinking about future directions, and promote the more effective application of AI technologies in this field, ultimately benefiting human health through an accelerated drug development process.