Towards an AI co-scientist
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co-scientist recapitulated unpublished experimental results via a parallel in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.
67bfd957c2a9b64ab3f97aa7
Juraj Gottweis
67bfd957c2a9b64ab3f97aa8
Wei-Hung Weng
67bfd957c2a9b64ab3f97aa9
Alexander Daryin
67bfd957c2a9b64ab3f97aaa
Tao Tu
67bfd957c2a9b64ab3f97aab
Anil Palepu
67bfd957c2a9b64ab3f97aac
Petar Sirkovic
67bfd957c2a9b64ab3f97aad
Artiom Myaskovsky
67bfd957c2a9b64ab3f97aae
Felix Weissenberger
67bfd957c2a9b64ab3f97aaf
Keran Rong
67bfd957c2a9b64ab3f97ab0
Ryutaro Tanno
67bfd957c2a9b64ab3f97ab1
Khaled Saab
67bfd957c2a9b64ab3f97ab2
Dan Popovici
67bfd957c2a9b64ab3f97ab3
Jacob Blum
67bfd957c2a9b64ab3f97ab4
Fan Zhang
67bfd957c2a9b64ab3f97ab5
Katherine Chou
67bfd957c2a9b64ab3f97ab6
Avinatan Hassidim
67bfd957c2a9b64ab3f97ab7
Burak Gokturk
67bfd957c2a9b64ab3f97ab8
Amin Vahdat
67bfd957c2a9b64ab3f97ab9
Pushmeet Kohli
67bfd957c2a9b64ab3f97aba
Yossi Matias
67bfd957c2a9b64ab3f97abb
Andrew Carroll
67bfd957c2a9b64ab3f97abc
Kavita Kulkarni
67bfd957c2a9b64ab3f97abd
Nenad Tomasev
67bfd957c2a9b64ab3f97abe
Yuan Guan
67bfd957c2a9b64ab3f97abf
Vikram Dhillon
67bfd957c2a9b64ab3f97ac0
Eeshit Dhaval Vaishnav
67bfd957c2a9b64ab3f97ac1
Byron Lee
67bfd957c2a9b64ab3f97ac2
Tiago R D Costa
67bfd957c2a9b64ab3f97ac3
José R Penadés
67bfd957c2a9b64ab3f97ac4
Gary Peltz
67bfd957c2a9b64ab3f97ac5
Yunhan Xu
67bfd957c2a9b64ab3f97ac6
Annalisa Pawlosky
67bfd957c2a9b64ab3f97ac7
Alan Karthikesalingam
67bfd957c2a9b64ab3f97ac8
Vivek Natarajan
Towards an AI co-scientist
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.
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.
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