邁向AI協作科學家

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

科學發現依賴於科學家提出新穎假設並進行嚴格的實驗驗證。為增強這一過程，我們引入了一位AI合作科學家，這是一個基於Gemini 2.0的多智能體系統。該AI合作科學家旨在幫助揭示新的原創知識，並基於先前證據，結合科學家提供的研究目標和指導，制定可證明新穎的研究假設和提案。系統設計採用了生成、辯論和進化的假設生成方法，靈感來自科學方法，並通過擴展測試時計算資源來加速。關鍵貢獻包括：(1) 一個多智能體架構，配備異步任務執行框架，以實現靈活的計算擴展；(2) 一個錦標賽進化過程，用於自我改進的假設生成。自動化評估顯示，測試時計算資源的持續投入提高了假設質量。雖然系統具有通用性，但我們主要聚焦於三個生物醫學領域的開發和驗證：藥物再利用、新靶點發現以及解釋細菌進化和抗微生物耐藥性的機制。在藥物再利用方面，系統提出的候選藥物顯示出有前景的驗證結果，包括針對急性髓性白血病的候選藥物，其在臨床適用濃度下顯示出體外腫瘤抑制作用。在新靶點發現方面，AI合作科學家提出了肝纖維化的新表觀遺傳靶點，並通過人肝類器官中的抗纖維化活性和肝細胞再生得到驗證。最後，AI合作科學家通過並行的計算模擬發現了一種細菌進化中的新基因轉移機制，重現了未發表的實驗結果。這些結果，詳見同期發布的報告，展示了增強生物醫學和科學發現的潛力，並預示著AI賦能科學家時代的到來。

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.