Publications

Kitano, H., Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst Biol Appl 7, 29 (2021). https://doi.org/10.1038/s41540-021-00189-3
Kitano, H., Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery. AI Magazine, 37(1), 39-49 (2016). https://doi.org/10.1609/aimag.v37i1.2642

The following publications are potentially relevant to the Nobel Turing Challenge.
It is listed for reference - for any new publications relevant to the challenge, please let us know.

2023

Dama, A.C., Kim, K.S., Leyva, D.M., Lunkes, A.P., Schmid, N.S., Jijakli, K. and Jensen, P.A. BacterAI maps microbial metabolism without prior knowledge. Nature Microbiology (2023). https://doi.org/10.1038/s41564-023-01376-0
Cornelio, C., Dash, S., Austel, V., Tyler, R., Josephson, T.R., Goncalves, J., Clarkson, K.L., Megiddo, N., Khadir, B.E. and Horesh, L. Combining data and theory for derivable scientific discovery with AI-Descartes. Nature Communications 14, 1777 (2023). https://doi.org/10.1038/s41467-023-37236-y
Boiko, D.A., MacKnight, R., and Gomes, G. Emergent autonomous scientific research capabilities of large language models. arXiv:2304.05332 [physics.chem-ph] (2023). https://doi.org/10.48550/arXiv.2304.05332
Yang Jeong Park, Daniel Kaplan, Zhichu Ren, Chia-Wei Hsu, Changhao Li, Haowei Xu, Sipei Li, Ju Li. Can ChatGPT be used to generate scientific hypotheses? Mar 2023. https://doi.org/10.48550/arXiv.2304.12208

2022

Kanda, G.N., Tsuzuki, T., Terada, M., Sakai, N., Motozawa, N., Masuda, T., Nishida, M., Watanabe, C.T., Higashi, T., Horiguchi, S.A., Kudo, T., Kamei, M., Sunagawa, G.A., Matsukuma, K., Sakurada, T., Ozawa, Y., Takahashi, M., Takahashi, K. and Natsume, T. Robotic search for optimal cell culture in regenerative medicine. eLife 11:e77007. https://doi.org/10.7554/eLife.77007

2021

Raayoni, G., Gottlieb, S., Manor, Y., Pisha, G., Harris, Y., Mendlovic, U., Haviv, D., Hadad, Y., and Kaminer, I. Generating conjectures on fundamental constants with the Ramanujan Machine. Nature 590, 67–73 (2021). https://doi.org/10.1038/s41586-021-03229-4
Davies, A., Veličković, P., Buesing, L. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 70–74 (2021). https://doi.org/10.1038/s41586-021-04086-x

2020

Burger, B., Maffettone, P.M., Gusev, V.V., Aitchison, C.M., Bai, Y., Wang, X., Li, X., Alston, B.M., Li, B., Clowes, R., Rankin, N., Harris, B., Sprick, R.S., and Cooper, A.I. A mobile robotic chemist. Nature 583, 237–241 (2020). https://doi.org/10.1038/s41586-020-2442-2
Murari, A., Peluso, E., Lungaroni, M. et al. Data driven theory for knowledge discovery in the exact sciences with applications to thermonuclear fusion. Sci Rep 10, 19858 (2020). https://doi.org/10.1038/s41598-020-76826-4
Udrescu SM, Tegmark M. AI Feynman: A physics-inspired method for symbolic regression. Sci Adv. 2020 Apr 15;6(16):eaay2631. doi: 10.1126/sciadv.aay2631. PMID: 32426452; PMCID: PMC7159912.

2019

Coutant, A., Roper, K., Trejo-Banos, D., Bouthinon, D., Carpenter, M., Grzebyta, J., Santini, G., Soldano, H., Elati, M., Ramon, J., Rouveirol, C., Soldatova, L.N. and King, R.D. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. PNAS 116, 18142–18147 (2019). https://doi.org/10.1073/pnas.1900548116

2016

Raccuglia, P., Elbert, K.C., Adler, P.D.F., Falk, C., Wenny, M.B., Mollo, A., Zeller, M., Friedler, S.A., Schrier, J., and Norquist, A.J. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016). https://doi.org/10.1038/nature17439
Naik AW, Kangas JD, Sullivan DP, Murphy RF. Active machine learning-driven experimentation to determine compound effects on protein patterns. Elife. 2016 Feb 3;5:e10047.https://doi.org/10.7554/eLife.10047. PMID: 26840049; PMCID: PMC4798950.

2014

Kangas, J.D., Naik, A.W. & Murphy, R.F. Efficient discovery of responses of proteins to compounds using active learning. BMC Bioinformatics 15, 143 (2014). https://doi.org/10.1186/1471-2105-15-143

2011

Murphy, R.F. An active role for machine learning in drug development. Nature Chemical Biology 7, 327-330 (2011). https://doi.org/10.1038%2Fnchembio.576

2010

Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M.N., Liakata, M., Markham, M., Rowland, J., Soldatova, L.N., Whelan, K.E., Young, M. and King, R.D. Towards Robot Scientists for autonomous scientific discovery. Autom Exp 2, 1 (2010). https://doi.org/10.1186/1759-4499-2-1

2009

King, R.D., Rowland, J., Oliver, S.G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L.N., Sparkes, A., Whelan, K.E. and Clare, A. The Automation of Science. Science 324, 85-89 (2009). https://doi.org/10.1126/science.1165620