en.Wedoany.com Reported - On May 21, 2026, in Beijing, China, the China Academy of Information and Communications Technology (CAICT), together with China National Pharmaceutical Group (Sinopharm), Hengrui Medicine, AI pharmaceutical companies, digital and intelligent service providers, research institutes, regulatory agencies, and industry associations, jointly established the "Pharmaceutical Industry Digital and Intelligent Transformation Promotion Center." The inaugural meeting is scheduled to be held in Beijing on June 2. Following its establishment, participating units will deploy artificial intelligence technologies in drug research and development, production management, quality control, and logistics distribution, achieving systematic application of data processing and process optimization.
China National Pharmaceutical Group and research institutes will provide hundreds of thousands of pieces of information on new drug molecules, compound structures, and clinical trial data. Algorithm teams will use machine learning to analyze molecular activity, efficacy, and toxicity, screen potential candidate molecules, and generate recommended sequences based on experimental priority. Laboratories will use historical drug data for model training, enabling the system to quickly identify molecular combinations with research and development value, significantly shortening experimental screening time.
Hengrui Medicine and China National Pharmaceutical Group have installed sensors and data acquisition equipment at production bases in Beijing, Shanghai, Guangzhou, and other locations to record real-time production environment parameters, including temperature, humidity, pressure, and dispensing accuracy. The system inputs data into an intelligent analysis platform, automatically generating maintenance plans and process adjustment recommendations. Tests show that equipment anomaly response time has been shortened to one-third of traditional processes, with production stability and product consistency meeting corporate standards.
Image recognition and intelligent inspection systems have been introduced in the quality inspection process to automatically scan drug appearance, packaging, and labels. Detected defects immediately generate reports and notify operators for handling, while the system analyzes process risk points based on historical data to provide references for process optimization. In practical application, the automatic inspection accuracy rate exceeds 98%, reducing the manual miss rate and improving overall production efficiency.
Full-process data collection has been implemented for logistics distribution and inventory management. Traceable information is generated for each drug product during outbound, transportation, and inbound processes. The platform calculates transportation duration, temperature changes, and inventory status, and automatically adjusts distribution plans. JD Logistics collaborates with the technical team to deploy monitoring nodes in key cities nationwide, enabling cross-enterprise information sharing and anomaly alerts, ensuring the safety and compliance of drugs during transportation and warehousing.
The joint laboratory standardizes data from different production bases and research and development units for training and optimizing machine learning models, and tests model performance in drug screening, production monitoring, and logistics management within simulated environments. Test results show that data processing speed has increased by 2.5 times compared to traditional methods, production decision-making time has been reduced by over 50%, and the execution efficiency of process optimization plans has significantly improved.
The center establishes a training system to provide enterprise technical personnel and researchers with training in algorithm application, data analysis, and equipment operation. The training content covers molecular screening model application, production line data analysis, intelligent inspection system operation, and logistics information management. Participants will receive operational qualification certification. Training is conducted simultaneously nationwide and combined with online platforms for refresher training and technical exchanges.
Industry associations formulate data interface standards, algorithm model evaluation indicators, and equipment performance testing procedures. Regulatory agencies supervise standard implementation and data compliance, ensuring that drug research and development, production, and distribution processes meet regulatory requirements. All participating units will operate according to the standards and regularly submit compliance reports, forming a nationwide artificial intelligence application system.
The establishment of the center marks the first step towards the systematic application of artificial intelligence technology in drug research and development, production management, quality inspection, and logistics distribution within China's pharmaceutical industry. Through multi-party collaboration and technology implementation, enterprises will deploy a unified platform at production and research bases in different cities across the country, achieving data sharing, process optimization, and unified equipment management, providing continuous technical support for future new drug research and development and production.
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