title: Towards an AI co-scientist
summary: 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  co-scientist recapitulated unpublished experimental results via a parallel in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.
paper_authors._id: 67bfd957c2a9b64ab3f97aa7
paper_authors.name: Juraj Gottweis
paper_authors._id: 67bfd957c2a9b64ab3f97aa8
paper_authors.name: Wei-Hung Weng
paper_authors._id: 67bfd957c2a9b64ab3f97aa9
paper_authors.name: Alexander Daryin
paper_authors._id: 67bfd957c2a9b64ab3f97aaa
paper_authors.name: Tao Tu
paper_authors._id: 67bfd957c2a9b64ab3f97aab
paper_authors.name: Anil Palepu
paper_authors._id: 67bfd957c2a9b64ab3f97aac
paper_authors.name: Petar Sirkovic
paper_authors._id: 67bfd957c2a9b64ab3f97aad
paper_authors.name: Artiom Myaskovsky
paper_authors._id: 67bfd957c2a9b64ab3f97aae
paper_authors.name: Felix Weissenberger
paper_authors._id: 67bfd957c2a9b64ab3f97aaf
paper_authors.name: Keran Rong
paper_authors._id: 67bfd957c2a9b64ab3f97ab0
paper_authors.name: Ryutaro Tanno
paper_authors._id: 67bfd957c2a9b64ab3f97ab1
paper_authors.name: Khaled Saab
paper_authors._id: 67bfd957c2a9b64ab3f97ab2
paper_authors.name: Dan Popovici
paper_authors._id: 67bfd957c2a9b64ab3f97ab3
paper_authors.name: Jacob Blum
paper_authors._id: 67bfd957c2a9b64ab3f97ab4
paper_authors.name: Fan Zhang
paper_authors._id: 67bfd957c2a9b64ab3f97ab5
paper_authors.name: Katherine Chou
paper_authors._id: 67bfd957c2a9b64ab3f97ab6
paper_authors.name: Avinatan Hassidim
paper_authors._id: 67bfd957c2a9b64ab3f97ab7
paper_authors.name: Burak Gokturk
paper_authors._id: 67bfd957c2a9b64ab3f97ab8
paper_authors.name: Amin Vahdat
paper_authors._id: 67bfd957c2a9b64ab3f97ab9
paper_authors.name: Pushmeet Kohli
paper_authors._id: 67bfd957c2a9b64ab3f97aba
paper_authors.name: Yossi Matias
paper_authors._id: 67bfd957c2a9b64ab3f97abb
paper_authors.name: Andrew Carroll
paper_authors._id: 67bfd957c2a9b64ab3f97abc
paper_authors.name: Kavita Kulkarni
paper_authors._id: 67bfd957c2a9b64ab3f97abd
paper_authors.name: Nenad Tomasev
paper_authors._id: 67bfd957c2a9b64ab3f97abe
paper_authors.name: Yuan Guan
paper_authors._id: 67bfd957c2a9b64ab3f97abf
paper_authors.name: Vikram Dhillon
paper_authors._id: 67bfd957c2a9b64ab3f97ac0
paper_authors.name: Eeshit Dhaval Vaishnav
paper_authors._id: 67bfd957c2a9b64ab3f97ac1
paper_authors.name: Byron Lee
paper_authors._id: 67bfd957c2a9b64ab3f97ac2
paper_authors.name: Tiago R D Costa
paper_authors._id: 67bfd957c2a9b64ab3f97ac3
paper_authors.name: José R Penadés
paper_authors._id: 67bfd957c2a9b64ab3f97ac4
paper_authors.name: Gary Peltz
paper_authors._id: 67bfd957c2a9b64ab3f97ac5
paper_authors.name: Yunhan Xu
paper_authors._id: 67bfd957c2a9b64ab3f97ac6
paper_authors.name: Annalisa Pawlosky
paper_authors._id: 67bfd957c2a9b64ab3f97ac7
paper_authors.name: Alan Karthikesalingam
paper_authors._id: 67bfd957c2a9b64ab3f97ac8
paper_authors.name: Vivek Natarajan
paper_title: Towards an AI co-scientist
paper_summary: 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.
paper_ai_summary: A multi-agent AI system named AI co-scientist aids in scientific discovery by generating and validating novel hypotheses across biomedical areas, demonstrating potential improvements in drug repurposing, target discovery, and bacterial evolution understanding.
paper_ai_keywords: multi-agent system Gemini 2.0 generate debate evolve device-free compute tournament evolution hypothesis generation drug repurposing novel target discovery liver fibrosis gene transfer mechanism bacterial evolution