21世纪经济报道记者 闫硕 在全球医药研发成本高企、成功率低迷的背景下，人工智能（AI）正以前所未有的速度重塑药物发现 与开发范式，成为全球科技与医药领域交叉创新的核心赛道。 回望2025年，AI制药正从概念验证迈入价值兑现初期，成为全球产业资本、政策制定者与科研机构共 同聚焦的战略高地。过去一年，AI制药领域投融资活跃、BD（商务拓展）合作密集、企业布局多元， 政策与产业共振，推动行业进入规模化发展的"快车道"。 北京中关村学院首席科学家刘海广向21世纪经济报道记者表示，传统药物研发常被认为是串行推进的过 程，但借助AI制药相关平台，有望改写这一固有模式。目前，在AI制药乃至整个AI for science（人工智 能驱动的科学研究）领域，不仅需要模型层面的创新设计，更需要解决数据方面的难题，要推动"数据 资本化"。 而随着模型创新与数据治理的逐步突破，行业也将迎来关键的发展拐点，市场对行业里程碑与核心参与 者的关注也日益升温。 国金证券分析师赵海春认为，从AI药企的角度，随着AI制药行业奇点来临，首个重要时点，必然是人 类首个AI驱动研发药物的获批上市。同时，因为AI制药本身是科技跨界的崭新赛道，未来的首 ...

21世纪经济报道记者 闫硕 在全球医药研发成本高企、成功率低迷的背景下，人工智能（AI）正以前所未有的速度重塑药物发现 与开发范式，成为全球科技与医药领域交叉创新的核心赛道。 回望2025年，AI制药正从概念验证迈入价值兑现初期，成为全球产业资本、政策制定者与科研机构共 同聚焦的战略高地。过去一年，AI制药领域投融资活跃、BD（商务拓展）合作密集、企业布局多元， 政策与产业共振，推动行业进入规模化发展的"快车道"。 北京中关村学院首席科学家刘海广向21世纪经济报道记者表示，传统药物研发常被认为是串行推进的过 程，但借助AI制药相关平台，有望改写这一固有模式。目前，在AI制药乃至整个AI for science（人工智 能驱动的科学研究）领域，不仅需要模型层面的创新设计，更需要解决数据方面的难题，要推动"数据 资本化"。 近年来，AI技术的突破为打破这一困局提供了新可能。不过，早期AI赋能模式仍存在局限，多是研发 人员借助计算机在已知化合物库中进行"大海捞针"式筛选，寻找具有潜在生物活性的分子，再通过合成 或采购后开展实验。这种模式不仅效率低下、有效分子命中率低，还面临专利风险。 郭晋疆指出，融合生成式人工智能 ...

2025年，数据要素市场化改革步入"深水区"。政策持续发力，技术加速迭代，企业如何抓住数据要素带来的战略红利，将海量数据真正转化为驱动增长的 核心资产？ 由大数据技术标准推进委员会（CCSA TC601）组织百家领先企业和权威专家共同编制的《数据资产管理实践指南（8.0版）》正式发布！作 为连续九年迭代的行业"风向标"，今年的8.0版首次系统描绘了从 "数据资源化"到"数据资产化"再到"数据资本化" 的完整价值跃迁路径，是各行各业管理 者、数据从业者把握未来方向的必备工具书。 一、核心升级：首次清晰勾勒数据价值化"三级跳" 过去，我们谈数据治理，多聚焦于质量、安全和内部应用。8.0版指南明确指出，数据资产管理已发生范式跃迁： 资源化（打好地基）：解决数据"有没有、好不好、安不安全"的问题，是价值释放的前提。指南详解八大管理职能（模型、标准、质量、安全等）的内在 联系与智能化演进。 资产化（实现价值）：核心是让数据的价值"看得见、摸得着、可计量"。指南系统阐释了数据资产登记、确权、估值、成本核算、入表、内外部流通六大 核心管理活动，直击企业最关心的"如何入表"、"如何定价"、"如何交易"等实操难题。 资本化（放 ...

专题：2025中国企业竞争力年会 "2025中国企业竞争力年会"于12月9日至10日在北京举行。贵阳大数据交易所董事长陈蔚指出，贵阳数 据交易所明确三大核心定位：一是公共数据价值化的核心服务者，依托贵阳大数据产业优势，打通公共 数据从资源目录管理、统一授权、综合治理到产品开发、合规上市的全链条服务；二是市场信任体系的 坚定构筑者，以 "裁判员 + 服务员" 角色构建合规、安全、高效的可信基础设施，降低交易各方成本与 风险，而非追求自身商业利益最大化；三是全国统一大市场的积极协同者，参与跨区域、跨层级规则对 接与标准互认，为统一开放、竞争有序的市场格局贡献贵州经验。 在具体实践层面，贵阳数据交易所通过 "三维举措" 推动落地： 其一，夯实基础公共服务体系。作为全国首个公共资源数据要素交易平台，严格遵循 "公正、公平、公 开" 原则，为全行业市场主体提供一站式公益性服务，降低参与门槛，优化入驻流程，构建多元参与的 市场生态；同时全力打造集数据资源登记、产权登记、资产登记于一体的递进式登记服务平台，厘清各 环节权利边界，为数据资产化、资本化奠定基础。 其二，以 "五位一体" 培育全链条生态。通过 "定规则、强合规、 ...

Core Insights - The article discusses the significant breakthrough in the financialization of data assets through the issuance of data asset ABS (Asset-Backed Securities), which has clarified the pathways for monetizing data in the capital market [1][2] Summary by Sections Data Asset ABS Overview - Data asset ABS refers to securities backed by stable and predictable cash flows generated from data assets, making data the primary source of revenue [2] - Data asset empowerment ABS involves using data as a credit enhancement tool embedded in traditional assets, enhancing cash flow stability and pricing efficiency [2] Market Activity - As of now, seven data asset ABS products have been successfully issued in 2023, with a total issuance scale of 2.49 billion yuan [1] - The products are categorized into two main types: four data asset ABS and three data asset empowerment ABS [1] Participation and Impact - State-owned enterprises have emerged as the primary initiators in the data asset ABS market, accounting for 71.4% of the projects, while private and public enterprises contributed to the remaining [2] - The average coupon rate for the seven projects is 2.04%, significantly lower than the average interest rate for traditional loans to small and medium-sized enterprises [2] Significance for Innovation and Financing - Data asset ABS provides a new financing channel for technology-driven and asset-light companies, facilitating better planning and operation of data and intellectual property assets [3] - The scale of data asset ABS enriches the asset allocation categories in the capital market, offering stable investment opportunities for conservative institutional investors [3] Challenges and Future Directions - Current challenges include the stability and predictability of revenue models, as some products have not yet established sustainable cash flow [4] - Recommendations for future development include balancing policy incentives with data privacy protection, leveraging technology for value discovery, and designing products that match investor risk preferences [4][5]

Core Viewpoint - The conference highlighted the current market's core contradictions and long-term opportunities, focusing on the recovery logic of the domestic economy, trends in the A-share market, and the direction of the 14th Five-Year Plan [1] Economic Indicators - Key indicators PPI and M2-M1 were emphasized, with PPI indicating a potential improvement in income and profits for component stocks, supporting the possibility of index growth [3] - The narrowing gap between M2 and M1 over the past year reflects a shift in market liquidity from stagnation to activity, providing solid support for equity assets [3] A-share Market Outlook - An optimistic outlook for the A-share market was presented, with expectations that China could transform its "strategic advantages" into "strategic victories" within three years, focusing on sectors linked to strategic advantages such as circular economy, AI computing power autonomy, and control of key materials [3][4] Financial and Monetary Reform - The importance of leveraging China's trade scale and supply chain advantages to promote the "tokenization" of core assets in the Hong Kong market was discussed, predicting that the RMB could capture 20% of global financial transactions in the next five years, reshaping the global monetary landscape and bringing incremental funds to the A-share market [3] Data as a Core Element - The next credit cycle's core element is identified as "data," with a focus on transforming data income into "credit consensus" to reconstruct balance sheets for governments, enterprises, and households, which is seen as the only solution to current fiscal and credit issues [4] Long-term Investment Strategy - The current market's short-term fluctuations are viewed as "interludes in a long-term trend," urging investors to focus on "strategic advantage sectors" rather than short-term speculation [4] - The future performance of the A-share market is expected to depend on the speed of industrial realization in these strategic sectors, emphasizing the importance of aligning with national strategies [4]

Core Insights - The first Qinghai Data Element Ecological Conference will be held on September 21, focusing on the construction of a data element ecosystem in Qinghai, which is a significant step in implementing the national "data element ×" three-year action plan and participating in the Belt and Road Initiative [1][2] - Qinghai aims to integrate its unique resource endowments and strategic positioning to promote the deep integration of the real economy and the digital economy, supporting the new era of western development strategy [1] - The conference will serve as a platform for policy interpretation and development trend analysis, featuring key experts discussing the role of data elements in driving industrial upgrades and economic transformation in Qinghai [2] Group 1 - The conference is co-hosted by multiple government departments and aims to build a cross-regional collaboration platform to unleash data value for high-quality development in Qinghai and the western region [2] - Qinghai is leveraging its clean energy advantages, such as solar and wind power, to establish a comprehensive computing power supply system, enhancing its competitive edge in the national data element layout [1][2] - The event will include a special seminar on "Data Empowering Industrial Development," focusing on the transformation of data from resources to assets and capital, with discussions on compliance and data asset integration [3] Group 2 - The conference will showcase various initiatives, including the unveiling of the Qinghai Data Element Circulation Service Innovation Center and the launch of a talent cultivation plan for data elements in Xining [3] - Experts from renowned data groups and technology companies will share advanced experiences and case studies to promote the deep integration of Qinghai's advantageous industries with data elements [3] - The discussions will also address the intelligent transformation of traditional industries and innovations in artificial intelligence applications, aligning with Qinghai's unique characteristics [3]

Group 1 - The core theme of the recent Global Digital Economy Conference is "Building Digital Friendly Cities," emphasizing the importance of global digital economic cooperation and the challenges faced by major cities in China, such as Beijing, Shanghai, and Shenzhen, in becoming global benchmarks for digital economy [1] - The transformation of digital cities is now a strategic mission aimed at enhancing national competitiveness and modernizing governance, moving beyond mere technological applications to a focus on public value [1][2] - The relationship between value and technology is crucial, where technology should serve as a means to solve urban issues and enhance citizen welfare rather than being an end in itself [2] Group 2 - The development of digital cities must address the new contradictions and core relationships, particularly focusing on data value attribution, distribution, and realization [3] - The process of data value realization involves three stages: data resourceization, data assetization, and data capitalization, each addressing different aspects of data management and economic value creation [3][4] - Cities should adopt data value management as a top-level strategy, establishing clear data ownership and efficient circulation systems, exploring equitable data revenue distribution models, and creating mechanisms for data value release driven by real-world applications [4]

Core Insights - The book "Data Capital" proposes a new framework for a fair data economy, exploring the capitalization of data across various sectors such as healthcare and finance, and aims to create a "smart economy" through trusted technology and artificial intelligence [1][4] Group 1: Data Cooperatives - The concept of a "data cooperative" is introduced to facilitate data sharing among citizens and enhance data innovation capabilities [4] - Establishing data property rights and sharing mechanisms can lead to the tokenization of data and its infrastructure, reshaping traditional banking and financial systems [4] Group 2: Economic Implications - The development of data algorithms and AI is expected to create new forms of economic participation, necessitating a more resilient social system [4] - The book emphasizes the risks and uncertainties in the new economic ecosystem, which directly challenges existing financial systems [4] Group 3: Digital Currency and Tokenization - The latter part of the book focuses on digital currency systems, tokenized financial ecosystems, stablecoins, and virtual asset trading networks, which are closely related to digital currencies [4] - The authors provide insights into the fundamental principles, roles, and future expectations of these digital currency-related topics, marking them as the most essential parts of the book [4]

Group 1 - The trend of data asset value realization is becoming more prominent, with more data being transformed into high-value assets, supported by financial market innovations that open diverse financing channels for related companies [1] - Experts predict that the market-oriented allocation mechanism for data factors will continue to improve, accelerating the process of data value release, which is expected to become a core engine driving high-quality development of the digital economy [1] Group 2 - Aopu Mai Biotechnology Co., Ltd. announced plans to acquire 100% of Pengli Biopharmaceutical Technology (Shanghai) Co., Ltd. through a combination of share issuance and cash payment, highlighting the importance of data as a valuable resource for companies [2] - Companies are increasingly focusing on building comprehensive data management systems to enhance data quality and extract data value, with many establishing specialized data management teams [2][3] Group 3 - A growing number of listed companies have appointed Chief Data Officers to strengthen data strategy and promote deep integration of data resources with business operations [3] - Companies are leveraging professional systems to achieve intelligent and refined data asset management, utilizing data asset management platforms and AI technologies to maximize data value [3] Group 4 - The process of data asset capitalization is crucial, with the inclusion of data assets in financial statements providing solid support for the confirmation, measurement, and disclosure of data value [4] - In the first quarter of 2025, 91 A-share listed companies included data assets in their financial reports, with a total scale of 4.402 billion, marking a significant increase in both the number of participating companies and the scale of inclusion compared to the previous year [4] Group 5 - Data asset inclusion in financial statements enhances resource allocation efficiency and facilitates precise capital market resource allocation, allowing for better monetization of data assets [5] - The successful listing of data assets, such as the smart parking data asset from Zhenjiang Transportation Industry Group, demonstrates the potential for data assets to be valued and traded in the market [5] Group 6 - The process of data capitalization has accelerated significantly under the drive of financial innovation, with examples such as the issuance of asset-backed securities based on operational and public service data [6] - The approval of the first data center REITs in the country further enriches the practice paths for data capitalization, injecting new momentum into industry development [6] Group 7 - The Chairman of the China Securities Regulatory Commission indicated ongoing support for technology companies to utilize intellectual property and data assets for asset securitization and REITs, signaling future policy support and financial innovation to promote the development of the data factor market [7] - Standardization of entity economic data is essential for transforming it into data required by capital markets, which is a primary task of financial technology [7]