On January 14, 2026, the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) jointly released the Guiding Principles of Good AI Practice in Drug Development, marking the first consensus framework reached by the world's two major pharmaceutical regulatory authorities on the application of artificial intelligence (AI) in drug development. This document outlines 10 core principles that cover the entire lifecycle of AI systems, from design and de-velopment to evaluation and management. It provides clear guidance on regulatory expectations for the pharmaceutical industry to leverage AI technology to empower drug development. This paper systematically analyzes the release background, core content and regulatory logic of these guiding principles, and on this basis extracts 5 core thinking models that run through them: risk-benefit balance, lifecycle management, traceability and transparency, multidisciplinary collaboration, and evidence generation and validation. Based on these 5 models, the article reveals the core characteristics of the document from 3 dimensions: "progressive layering", "principle-oriented and flexible adaptation", and "end-to-start closed-loop thinking". Drawing on practical observations of the AI assisted pharmaceutical industry, the paper discusses the practical guidance value for the pharmaceutical industry, compliance challenges, and the reshaping of drug development models. The study concludes that these guiding principles signify a paradigm shift in pharmaceutical regulation from “passive acceptance” to “active guidance” of AI technology applications. Future competition in the AI assisted pharmaceutical field will no longer be an arms race of algorithmic accuracy, but rather a contest of the degree of internalization of the above 5 thinking models. Companies that adopt these principles at an early stage are likely to gain a strategic advantage in their interactions with regulators. This document lays an important foundation for global regulatory coordination of AI assisted pharmaceutical development and also holds enlightening significance for China in building its own AI assisted pharmaceutical regulatory framework.

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