以下文章来源于36氪Pro ，作者海若镜 36氪Pro . 36氪旗下官方账号。深度、前瞻，为1%的人捕捉商业先机。 百亿美金涌入，AI制药"GPT时刻"来了。 文 ｜ 海若镜 来源｜ 36氪Pro（ID：krkrpro） 封面来源 ｜ Pixabay AI制药，临界点终于来了。 今年中国创新药出海交易火热，其中，AI制药公司正成为一股不容小觑的力量。2025年3月至8月期间，元思生肽、华深智药海外子公司、晶泰科技等接 连完成总金额数十亿美金的BD交易。 | 披露时间 | 卖方 | 买方 | 首付款/近期付款 | 潜在里程碑 及销售分成等总额 | 平台(管线阶段) | | --- | --- | --- | --- | --- | --- | | 2025/8/5 | 晶泰科技 | DoveTree | 5100万美元 | 58.9亿美元 | 多款临床前/早期的大 分子与小分子创新药 | | | | | | | 资产 (DoveTree 指 定的多个靶点,晶泰 | | | | | | | 按约研发) | | 2025/6/13 | 石药集团 | 阿斯利康 | 1.1亿美元 | 53 亿美元 | 基于石药AI平 ...

Core Insights - The article discusses the significant advancements in AI-driven drug development, highlighting the emergence of AI pharmaceutical companies as a formidable force in the industry [1][2] - It emphasizes the shift in drug discovery paradigms from traditional methods to AI-enabled rational design, which allows for the creation of novel molecules and proteins [2][3] Group 1: AI in Drug Development - AI pharmaceutical companies like YuanSi and Huashen have successfully completed multi-billion dollar business development transactions, showcasing their rapid growth and effectiveness in drug discovery [1] - The new wave of AI technology, particularly advancements like AlphaFold 2 and AlphaFold 3, has revolutionized protein structure prediction, significantly enhancing the drug design process [5][6] - AI models such as Chai-2 have demonstrated a remarkable increase in hit rates for antibody candidates, drastically reducing the time and cost associated with traditional drug discovery methods [7][8] Group 2: Industry Transformation - The traditional drug development process is being transformed, with AI enabling the design of drugs for previously challenging targets, potentially leading to breakthroughs in treating chronic diseases [8] - The article outlines three types of players in the AI pharmaceutical space: tech giants with substantial capital, startup teams led by top AI and biological scientists, and traditional pharmaceutical companies leveraging AI for drug development [10] - The integration of AI in drug development is expected to lead to a significant reshaping of the pharmaceutical industry, with biotech firms becoming centers for molecular design and large pharmaceutical companies focusing on clinical trials and commercialization [8][10] Group 3: Future Outlook - The article suggests that the future of drug development will increasingly rely on AI, with all new drug companies expected to incorporate AI to varying degrees [12] - The ability to generate high-quality biological experimental data will be crucial for teams aiming to develop high-performance AI models, indicating a shift towards data-driven approaches in drug discovery [12] - The convergence of AI and drug development is seen as a critical factor for the success of innovative drug discovery, with the potential for significant industry disruption in the coming years [11][12]

Core Insights - The article discusses the convergence of life sciences and digital internet technologies through AI for Science, highlighting the transformative potential of large models in accelerating scientific discovery [3][6]. - It emphasizes the shift from traditional trial-and-error methods in drug development, which typically require 10 years and $2 billion, to automated processes enabled by AI, significantly reducing costs and time [7][8]. Group 1: Background and Framework - The 1950s saw two revolutions: Shannon and Turing's information theory laid the groundwork for the digital revolution, while Watson and Crick's discovery of the DNA double helix initiated the information age in biology [6]. - The article introduces a mapping framework for understanding AI in life sciences, with axes representing Generalist vs. Specialist and Tech vs. Bio, assessing the breadth and depth of startups in biopharmaceutical development [9][11]. Group 2: Biology Foundation Models - AlphaFold 3 represents a milestone in AI for science, solving the long-standing challenge of protein structure prediction, which previously took months or years [14]. - Isomorphic Labs, a spinoff from Google DeepMind, has secured significant partnerships with Eli Lilly and Novartis, validating its technology's commercial value [15]. - Other models like ESM3 and Evo2 are exploring different paths in biological foundation models, focusing on multi-modal inputs and genome language modeling [17][22]. Group 3: AI Scientist and Automation - The AI Scientist concept aims to automate research processes, addressing the inefficiencies of traditional biological research, which is often lengthy and costly [24]. - FutureHouse is developing a multi-agent system to enhance research efficiency, demonstrating the potential for AI to significantly increase productivity in scientific discovery [38]. Group 4: AI-native Therapeutics - AI-native therapeutics companies aim to integrate AI throughout the drug discovery and clinical development process, focusing on complex therapies like RNA and cell therapies [40]. - Companies like Xaira Therapeutics and Generate Biomedicines are building comprehensive platforms that leverage AI for end-to-end drug development, aiming to reduce time and costs associated with traditional methods [49][51]. Group 5: AI Empowered Solutions - Companies in this category focus on optimizing specific stages of drug development using AI, such as drug repurposing and clinical trial acceleration [68][75]. - Tahoe Therapeutics has released a large single-cell perturbation dataset, enhancing AI model training and drug discovery processes [64]. Group 6: Conclusion - The article concludes that the integration of foundation models and automated AI scientists is driving exponential advancements in scientific exploration, shifting value from traditional CROs to AI-native companies [78].