Objective: To respond to the State Council's “Artificial Intelligence(AI) Plus” initiative, the study systematically investigated the objectives, key actions, and implementation pathways for “AI Plus” into drug regulation, aiming to support the establishment of an intensive, efficient, safe, and controllable “AI Plus Drug Regulation” system. Methods: The study was conducted through policy analysis of national AI strategies and their implications for drug regulation, review of current practices and challenges in the field. On this basis, combined with industry characteristics, the stage goals of implementing the “AI Plus” action were proposed in the field of drug regulation. A six-in-one key actions plan centered on the six elements of “data, computing power, models, scenarios, governance, and ecology” was constructed. Results: A three-stage target for implementation was proposed, and a framework for “AI Plus Drug Regulation” centered on the six elements was designed. Additionally, a comprehensive guarantee system, including organizational mechanisms, resource investment, and standardization specifications was established to ensure effective execution. Conclusion: The structured implementation pathways and comprehensive safeguards can facilitate the deep “AI Plus” into drug regulation, thereby providing technical support to enhance regulatory efficiency and fostering innovation in the pharmaceutical industry.

Objective: The rapid development of Artificial Intelligence (AI) is triggering a paradigm shift in pharmaceutical testing through its robust capabilities in data processing, analytical recognition, and content generation. This paper systematically summarized the empowerment of pharmaceutical testing by AI to provide strategic insights for implementing the national “AI Plus” initiative, advancing the modernization of pharmaceutical testing, and bolstering the high-quality development of the pharmaceutical industry. Methods: Through literature review and comprehensive analysis, this study systematically evaluated the current application status of AI in pharmaceutical testing, identified critical challenges, and proposed potential future development pathways. Results: AI has been extensively applied in three primary domains: intelligent pharmaceutical testing and data analysis, digital standards, and digital reference materials. It serves as the key driver and technological cornerstone for the development of “Smart Laboratories”. However, its in-depth application still faces challenges such as data, algorithms, talents, regulations and standards. Consequently, recommendations for implementing AI within “Smart Pharmaceutical Testing” were proposed. Conclusion: AI is revolutionizing the research and application paradigms of pharmaceutical testing and accelerating its modernization process. Future strategies should focus on aligning with the “15th Five-Year Plan” and the “AI Plus” initiative to construct a comprehensive “Testing-Regulation-Industry” support system based on the “Smart Pharmaceutical Testing” blueprint. By consolidating data foundations, refining algorithmic models, strategically deploying AI agent technologies, and promoting the construction of “Smart Laboratories”, the transformation of theoretical research into practical scenarios can be accelerated, which will empower high-level drug safety and achieve high-quality development of the pharmaceutical industry.

Objective: To trace the technological evolution of artificial intelligence (AI) in drug discovery and design, focusing on the transition from discriminative intelligence represented by predictive models to generative intelligence centered on generative models, and to analyze its role in advancing innovative drug discovery paradigms. Methods: Through systematic literature review and case studies, the applications of predictive models and generative models in drug discovery and design were compared, and the practical experiences from AI-driven pharmaceutical platforms were summarized. Results: The study demonstrated that predictive models significantly enhanced drug screening efficiency and accuracy, while generative models enabled a leap from “compounds screening” to “novel molecules design”, driving the formation of automated and closed-loop drug discovery workflows. Conclusion: AI is steering drug discovery from “predictive optimization” toward “creative design”, and is reshaping the drug discovery paradigm. Future development directions include multimodal fusion, knowledge-enhanced generation, reinforcement learning-based closed-loop optimization, quantum computing integration, and regulatory framework refinement, which will further accelerate AI-driven novel drug discovery.

Objective: To promote the strategic deployment of “Intelligent Drug Regulation” in China, advance the practical innovation of “Artificial Intelligence(AI) Plus” models in the field of drug regulation, and to explore the application pathway of “AI Plus” in the field of drug inspection. Methods: Based on the Dify work platform, the study constructed the workflow for summarizing historical defects in the inspected unit and the chatflow for matching unknown defect clauses and determining their classification. The workflow integrated functional nodes, including database retrieval, data segmentation, iterative learning, and summarization, and evaluated its operational effectiveness by dividing defects into three classification levels (low, medium, and high) based on the defect base number. The chatflow completed the matching and classification determination of defect clauses through knowledge base retrieval and the semantic reasoning of large language models. It compared the accuracy of unknown defect clauses matching and the consistency of defect classification between human experts and large models based on the research data from the knowledge base and test sets. Results: The workflow performed well in low-, medium-, and high-defect baseline groups, enabling intelligent summarization of historical defects in inspected units. The chatflow achieved high accuracy in matching of unknown defect clauses, and its defect classification criteria were stricter than those of manual judgment, enabling intelligent matching and classification of unknown defect clauses. Conclusion: The workflow established in this study can intelligently summarize the historical defects of the inspected units, and chatflow can intelligently match and classify unknown defect clauses. It not only provided a useful exploration for the implementation of intelligent tools for drug inspection, but also provided important technical references for the construction of “Intelligent Drug Regulation” system and the integration framework of large language models in the field of drug regulation and the innovation of “AI Plus” regulation models in China.

Objective: To explore novel quality control strategies based on digital and intelligent technologies, analyze their innovative applications in the quality control of chemical drugs, traditional Chinese medicines (TCM), and biotechnological drugs, and thereby promote the digital and intelligent transformation of China's drug quality control system. Methods: By systematically reviewing the innovative applications of digital and intelligent technologies in the quality control of chemical drugs, TCM, and biotechnological drugs, relevant technical methods and implementation strategies were summarized. Current challenges in the digital and intelligent transformation, such as data quality and model interpretability, were also analyzed. Resultsand Conclusion: In the quality control of chemical drugs, digital and intelligent technologies have been implemented in areas such as polymorph screening, impurity analysis, production processes supervision, and appearance identification. Regarding the quality control of TCM, these technologies were also extensively applied in appearance authentication, manufacturing processes control, and quality evaluation. Furthermore, within the quality control system for biotechnological drugs, digital and intelligent technologies were integrated into the assessment of safety, stability, and efficacy. The integration of such technologies represented a crucial pathway for enhancing drug quality and safety levels, as well as facilitating the transformation of drug quality control models. In the future, it will be necessary to further address existing shortcomings in data quality and model interpretability, in order to establish a more systematic and accurate drug quality control system, thereby providing support for the digital and intelligent advancement of China's pharmaceutical industry.

Objective: By reviewing the history of the evaluation and approval of AIDS prevention and treatment drugs both at home and abroad, and summarizing the current characteristics of the development and registration of AIDS prevention and treatment drugs in China, this article aimed to provide references for the development and registration of new drugs for AIDS. Methods: A retrospective review was conducted on the history and current situation of the evaluation and approval of AIDS prevention and treatment drugs worldwide, especially in China. The work carried out by regulatory authorities, the industry, and academics to meet the clinical treatment, prevention and control needs of this disease was summarized, and future prospects were proposed. Resultsand Conclusion: Anti-HIV infection/Antiretroviral therapy (ART) drugs are the primary treatment drugs for AIDS. Over the past few decades, the significant advancements in global ART drugs have transformed AIDS from an incurable disease to a chronic one. The development of AIDS prevention and treatment drugs in China has gone through periods of no available drugs, disparity compared to the international level,drugs imported and generic drugs being dominant, and the approval of domestic innovative drugs. With the continuous encouragement of the development and registration of AIDS drugs with the goal of improving patient access to drugs, and the implementation of multiple measures to encourage drug innovation, China has basically established a clinical evaluation technical standard system for clinical trials of AIDS drugs, guiding the development of innovative drugs. Imported drugs are approaching simultaneous approval with global, with significant reductions in the gap in types, quantity and time. The types and quantities of generic drugs have been increasing, and several domestic ART innovative drugs have been approved for marketing and included in clinical guidelines. The launch of fixed-dose combination and long-acting formulations has further improved patient compliance. The treatment and prevention goals for different patient populations have been fully covered, effectively meeting the clinical needs of Chinese patients. In the future, attention will continue to be paid to the prevention and treatment areas with clinical needs, new dosage forms, new administration routes with significant advantages, and domestic innovative drugs, continuously encouraging the innovation of AIDS prevention and treatment drugs, constantly meeting the needs of treatment prevention and control, reducing the occurrence of complications, improving life quality, and prolonging lives.

Objective: By analyzing the inspection requirements for medical devices and the difficulties in innovative medical devices testing, suggestions for improving the testing level of innovative medical devices were proposed, to provide references for strengthening the risk control throughout the life cycle of innovative medical devices. Methods: The relevant regulations on pre-market testing, post-market testing, product technical requirements, and proficiency testing of innovative medical devices were sorted out. From the perspectives of the technical challenges of innovative medical devices, the risk awareness of registrants, and the capability level of testing institutions, the potential risks that affect the quality control level were analyzed. Results: It was necessary to strengthen the integrated linkage of research, evaluation, and testing, enhance testing supervision and risk research, and continuously improve the quality control level of innovative medical devices. Conclusion: Within the framework of the Medical Device Administration Law of the People's Republic of China, it is essential to consistently strengthen the risk awareness of registrants, improve the capability level of testing institutions, intensify the supervision and management of testing, enrich the scientific regulatory technical system, further leverage the role of testing, collectively ensure the safety baseline of medical devices, and promote the high-quality and innovative development of the industry.