Towards an AI co-scientist
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Gottweis, Juraj Weng, Wei-Hung Daryin, Alexander Tu, Tao Palepu, Anil Sirkovic, Petar Myaskovsky, Artiom Weissenberger, Felix Rong, Keran Tanno, Ryutaro Saab, Khaled Popovici, Dan Blum, Jacob Zhang, Fan Chou, Katherine Hassidim, Avinatan Gokturk, Burak Vahdat, Amin Kohli, Pushmeet Matias, Yossi Carroll, Andrew Kulkarni, Kavita Tomasev, Nenad Guan, Yuan Dhillon, Vikram Vaishnav, Eeshit Dhaval Lee, Byron Costa, Tiago R D Penadés, José R Peltz, Gary Xu, Yunhan Pawlosky, Annalisa Karthikesalingam, Alan Natarajan, Vivek |
| author_facet | Gottweis, Juraj Weng, Wei-Hung Daryin, Alexander Tu, Tao Palepu, Anil Sirkovic, Petar Myaskovsky, Artiom Weissenberger, Felix Rong, Keran Tanno, Ryutaro Saab, Khaled Popovici, Dan Blum, Jacob Zhang, Fan Chou, Katherine Hassidim, Avinatan Gokturk, Burak Vahdat, Amin Kohli, Pushmeet Matias, Yossi Carroll, Andrew Kulkarni, Kavita Tomasev, Nenad Guan, Yuan Dhillon, Vikram Vaishnav, Eeshit Dhaval Lee, Byron Costa, Tiago R D Penadés, José R Peltz, Gary Xu, Yunhan Pawlosky, Annalisa Karthikesalingam, Alan Natarajan, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards an AI co-scientist Gottweis, Juraj Weng, Wei-Hung Daryin, Alexander Tu, Tao Palepu, Anil Sirkovic, Petar Myaskovsky, Artiom Weissenberger, Felix Rong, Keran Tanno, Ryutaro Saab, Khaled Popovici, Dan Blum, Jacob Zhang, Fan Chou, Katherine Hassidim, Avinatan Gokturk, Burak Vahdat, Amin Kohli, Pushmeet Matias, Yossi Carroll, Andrew Kulkarni, Kavita Tomasev, Nenad Guan, Yuan Dhillon, Vikram Vaishnav, Eeshit Dhaval Lee, Byron Costa, Tiago R D Penadés, José R Peltz, Gary Xu, Yunhan Pawlosky, Annalisa Karthikesalingam, Alan Natarajan, Vivek Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning Physics and Society Other Quantitative Biology 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. |
| title | Towards an AI co-scientist |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning Physics and Society Other Quantitative Biology |
| url | https://arxiv.org/abs/2502.18864 |