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

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co-scientist recapitulated unpublished experimental results via a parallel in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.