Progress and Core Challenges of Artificial Intelligence Applications in the Pharmaceutical Field

The pharmaceutical innovation system covers core procedures including new drug development, research on disease molecular mechanisms, individualized diagnosis and treatment, and post-marketing drug safety supervision. Even though relevant research has made steady progress, the industry is plagued by multiple practical bottlenecks that hinder its development. Low overall R&D efficiency, incomplete clarification of molecular mechanisms behind complicated diseases, and limited practical capacity of precision therapy all slow the upgrading of modern pharmaceutical systems. Equipped with powerful tools to process massive datasets and mine hidden biological patterns, artificial intelligence (AI) has been adopted in many key pharmaceutical research areas over recent years. It has overturned the conventional experience-based research mode and steered pharmaceutical research toward an innovative data-driven framework. This paper sorts out real-world applications of AI across the full pharmaceutical lifecycle and elaborates on major barriers to its practical deployment, such as insufficient standardized medical data, low interpretability of algorithm models, and immature industrial regulatory systems. Continuous technological iterations will refine explainable AI algorithms, multi-source medical data integration techniques and privacy-preserving computing frameworks. These improvements can deepen the integration of AI and pharmacy, delivering solid support for innovative drug discovery and the clinical implementation of precision diagnosis and treatment.