药物研发作为现代医药产业的核心驱动力，面临传统模式“高投入、长周期、低产出”的困境，亟需突破以应对日益复杂的健康需求。人工智能（AI）技术的快速发展为药物研发带来了革命性变革，其在蛋白质结构预测、蛋白质设计、抗体药物设计及小分子药物设计等领域的应用显著提升了研发效率与成功率。深入分析了AI在蛋白质结构预测中的突破及其在靶点发现、虚拟筛选等环节的应用潜力；探讨了AI驱动蛋白质设计从结构预测到功能创新的闭环模式；剖析了AI在抗体序列优化、亲和力成熟及新型抗体设计中的作用；梳理了AI在小分子药物靶点识别、虚拟筛选及ADMET优化中的最新成果。指出AI应用中面临的数据质量、模型可解释性及实验验证等挑战，并展望了多模态数据融合、动态行为预测及自动化平台的未来发展方向。通过全面剖析AI赋能药物研发的现状与问题，旨在为加速新药创制、提升人类健康福祉提供科学视角与思考启示，提供一个关于AI赋能药物研发领域科技问题的全面且深入的视角，并激发对未来发展方向的思考，以期促进AI技术在药物研发领域的更有效应用，加速新药创制进程，最终惠及人类健康

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.