科学研究是人类创造新知识最重要的方式，一个经济体和社会唯有持续注入新知识，才能保持活力，新知识往往也催生新思想，不让文化变成死水一 潭。 但今天的科学离"人"越来越远了。它不像消费、娱乐和金融那样贴近日常生活，反而与公众之间隔着重重高墙，还有很多种"守门人"夹在中间。比如今 天分享的文章中谈论的"科学论文"，过去本质上是"学者写给学者看的"。 从英国皇家学会期刊的时代开始，用科学论文进行交流与融合的效率原本不低，这甚至是人类伟大的发明，是一种跟互联网一样重要的社会协议。但发 展到今天，这个系统已经把"科学家"和学生们都变成了某种"小白鼠"，想出各种方法激励他们写得更多、更快、更炫酷。这些代表人类理解自然和世界 最高水平的知识，如何变成真正可以为每个人所使用和受益的东西，已经不是他们追求的目标了。 也许科学研究应该有DAY 1"用户"的思维，而不只是为"人类老板"服务和声誉积分的比拼。人们开始把机器看待成"用户"和伙伴（peers），这是一个好 的开始。 | designation: | D2-002 | | --- | --- | | author : | andrew white | | status: | ...

Core Insights - Demis Hassabis, founder of DeepMind and Isomorphic Labs, has made significant contributions to AI, particularly in drug development and protein structure prediction, with his work leading to the 2024 Nobel Prize in Chemistry for AlphaFold [5][10][19] - Isomorphic Labs, established in 2021, focuses on AI-driven drug discovery, leveraging AlphaFold's technology to enhance the drug development process [3][10][19] Company Overview - Isomorphic Labs has developed a unified AI drug design engine that utilizes multiple next-generation AI models applicable across various therapeutic areas [3][10] - The company recently secured $600 million in funding, led by Thrive Capital, to further develop its AI drug design engine and advance treatment solutions into clinical stages [3][10] Technological Advancements - AlphaFold 3, released in May 2024, significantly improves the prediction of protein structures and molecular interactions, enhancing drug development efficiency by at least 50% compared to traditional methods [14][16] - The AI drug design engine integrates advanced AI technologies, including diffusion models and multi-task reinforcement learning, to streamline the drug discovery process, reducing the timeline from an average of 5-10 years to 1-2 years [16][17] Market Potential - The global AI drug discovery market is projected to reach $20 billion by 2025, with a compound annual growth rate exceeding 30% [19] - The industry is witnessing a surge in investment, with over a hundred startups and large pharmaceutical companies actively engaging in AI research and development [19][20] Strategic Collaborations - Isomorphic Labs has formed strategic partnerships with major pharmaceutical companies, including Novartis and Eli Lilly, to co-develop AI-assisted drug discovery projects [10][11] - These collaborations aim to explore challenging drug targets and expand the scope of AI applications in drug development [11][19]

Core Viewpoint - The discovery and identification of therapeutic targets remain a critical bottleneck in drug development, with over 90% of candidate drugs failing in clinical development due to flawed initial hypotheses regarding biological function, disease relevance, or druggability [2][3]. Group 1: Target Discovery Challenges - Traditional target discovery relies on disease biologists integrating various independent biomedical data to form testable hypotheses, which is a slow and costly process, often exceeding $2 million per target [2][3]. - The failure rate in clinical development is largely attributed to issues with the selected targets rather than the compounds themselves [2]. Group 2: Introduction of OriGene - A new multi-agent virtual disease biologist system named "OriGene" has been developed, focusing on target discovery and clinical translation value assessment, outperforming human experts and leading AI models in target discovery capabilities [2][3][9]. - OriGene autonomously discovered new targets for liver cancer and colorectal cancer, demonstrating its ability to generate original targets validated through experiments [3][27]. Group 3: System Features and Functionality - OriGene integrates over 500 expert tools and organized biomedical databases, supporting multi-modal reasoning across genomics, transcriptomics, proteomics, phenomics, and pharmacology [11][12]. - The system features a multi-agent collaborative decision-making architecture, including a Coordinator Agent, Planning Agent, Reasoning Agent, Critic Agent, and Reporting Agent, enabling a closed-loop autonomous scientific decision-making process [12][13]. Group 4: Performance Evaluation - A specialized benchmark test set for target discovery, TRQA, was created, covering 1,921 multi-dimensional validation questions, demonstrating OriGene's superior performance in accuracy, recall, and robustness compared to human experts and other AI models [18][21]. - The system's self-evolving capabilities allow it to improve its reasoning ability over time through iterative learning and feedback from experiments [14][16]. Group 5: Practical Validation - In liver cancer, OriGene identified G protein-coupled receptor GPR160 as a key target, showing significant expression in cancer tissues and potential as a new immune checkpoint [23]. - For colorectal cancer, the system selected arginase ARG2 as a target, confirming its high expression in cancer tissues and demonstrating effective tumor suppression in patient-derived organoid models [25][27]. Group 6: Implications for Drug Development - The research signifies a major advancement in using AI to accelerate therapeutic target discovery, providing a scalable and adaptable platform for identifying mechanism-based treatment targets [27]. - As generative AI models and biomedical data resources mature, frameworks like OriGene are expected to facilitate AI-driven end-to-end drug discovery, enhancing the potential for precision medicine [27].