迈向AI科研协作伙伴

Towards an AI co-scientist

February 26, 2025

作者: Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, Khaled Saab, Dan Popovici, Jacob Blum, Fan Zhang, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Pushmeet Kohli, Yossi Matias, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, José R Penadés, Gary Peltz, Yunhan Xu, Annalisa Pawlosky, Alan Karthikesalingam, Vivek Natarajan

cs.AI

摘要

科学发现依赖于科学家提出新颖假设并经过严格的实验验证。为增强这一过程，我们引入了一位AI科研助手，这是一个基于Gemini 2.0构建的多智能体系统。该AI科研助手旨在协助揭示新的原创知识，并基于现有证据，结合科学家提供的研究目标和指导，制定出可证明新颖的研究假设与提案。系统设计采用了生成、辩论与进化的假设生成方法，灵感源自科学方法，并通过扩展测试时计算资源加速实现。主要贡献包括：(1) 采用多智能体架构及异步任务执行框架，以实现灵活的计算扩展；(2) 引入锦标赛式进化过程，促进假设生成的自我优化。自动化评估显示，增加测试时计算资源持续提升假设质量。尽管系统通用，我们着重于三个生物医学领域的开发与验证：药物再利用、新靶点发现以及细菌进化与抗微生物耐药性机制的阐释。在药物再利用方面，系统提出的候选药物展现出有前景的验证结果，包括针对急性髓系白血病的候选药物，在体外实验中于临床适用浓度下显示出肿瘤抑制作用。在新靶点发现方面，AI科研助手提出了肝纤维化的新表观遗传靶点，通过人源肝类器官中的抗纤维化活性及肝细胞再生得到验证。最后，AI科研助手通过并行计算机模拟，重现了未发表的实验结果，发现了一种细菌进化中的新基因转移机制。这些成果，详见同期发布的独立报告，展示了增强生物医学与科学发现的潜力，预示着AI赋能科学家时代的到来。

English

Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance. The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute, improving hypothesis quality. While general purpose, we focus development and validation in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and anti-microbial resistance. For drug repurposing, the system proposes candidates with promising validation findings, including candidates for acute myeloid leukemia that show tumor inhibition in vitro at clinically applicable concentrations. For novel target discovery, the AI co-scientist proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids. Finally, the AI co-scientist recapitulated unpublished experimental results via a parallel in silico discovery of a novel gene transfer mechanism in bacterial evolution. These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists.