On October 25, 2024, the award ceremony for the national finals of the 2024 "Data Elements ×" Competition was held in Beijing. The competition was led by the National Data Administration and co-organized by 15 departments, including the Central Cyberspace Affairs Commission, Ministry of Transport, Ministry of Agriculture and Rural Affairs, Ministry of Commerce, Ministry of Culture and Tourism, National Health Commission, Ministry of Emergency Management, State Administration of Financial Regulation, China Securities Regulatory Commission, National Healthcare Security Administration, Chinese Academy of Sciences, China Meteorological Administration, National Cultural Heritage Administration, and National Administration of Traditional Chinese Medicine.
Spanning five months, the competition adopted a "local preliminaries + national finals" format and is the first national competition in China focusing on the development and application of data elements. A total of 19,000 teams and 100,000 participants from government, industry, academia, research, and application sectors across the country competed fiercely in 12 tracks, including industrial manufacturing, modern agriculture, commercial circulation, transportation, financial services, technological innovation, cultural tourism, healthcare, emergency management, meteorological services, urban governance, and green low-carbon development. The competition organizing committee selected 120 teams for roadshow defense through online evaluation, conducting comprehensive assessments from perspectives such as demonstration value, effectiveness, and advancement.Finally, first, second, and third prizes were awarded in each track (1 first prize, 2 second prizes, and 3 third prizes per track), along with several special awards for distinctive achievements. After multiple rounds of intense competition, the "AI + Protein Design" project from Peking University Shenzhen Graduate School won the First Prize in Guangdong Province and the Second Prize in the national finals.
To explore the fourth paradigm of scientific research in the field of protein design, Associate Professor Chen Jie from the School of Information Engineering and Professor Mao Youdong from the School of Physics at Peking University formed a joint R&D team for "AI + Protein Design." Driven by both data and algorithms, the team aims to improve the efficiency of protein design and achieve controllable protein engineering. It is expected to support the transformation of drug development paradigms, enabling more targeted drugs with fewer toxic side effects while significantly enhancing the efficiency and controllability of drug research and development.
The "AI + Protein Design" team’s project focuses on exploring data-driven paradigms for protein design research. In 2020, Demis Hassabis and John M. Jumper from Google DeepMind — winners of the 2024 Nobel Prize in Chemistry — developed AlphaFold2, which achieved outstanding performance in the Critical Assessment of Protein Structure Prediction (CASP) and solved a major biological challenge that had persisted for 50 years. Since then, scientists have begun exploring data-driven protein design methods to improve the efficiency of drug development and reduce its time costs.
The team has made significant progress in the field of AI + protein design. They proposed a new cryo-electron microscopy imaging algorithm to reconstruct the dynamic free energy landscape of proteasome substrate degradation and collected unique four-dimensional cryo-electron microscopy data. Based on this database, they simulated the continuous conformational dynamics of proteasomes and developed a world-leading (First-in-Class) target mechanism (Nature605: 567-574 (2022)). Using big data for multi-modal large model training, they conducted protein design, generation, and screening, and have commercialized their achievements through collaborations with multiple enterprises. To date, the team has published 5 papers in top international academic journalsNatureandScience, along with dozens of papers inNaturesub-journals. They will continue to deepen research and innovation in AI-driven protein design.
Editing and Proofreading: Lilly
