Publications

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

• Merchant, A., Batzner, S., Schoenholz, S.S. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023). https://doi-org./10.1038/s41586-023-06735-9

• Jun Cheng et al., Accurate proteome-wide missense variant effect prediction with AlphaMissense.Science381,eadg7492(2023).DOI:10.1126/science.adg7492

• Boiko, D.A., MacKnight, R., Kline, B. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). https://doi-org/10.1038/s41586-023-06792-0

• Romera-Paredes, B., Barekatain, M., Novikov, A. et al. Mathematical discoveries from program search with large language models. Nature (2023). https://doi-org/10.1038/s41586-023-06924-6

• Sanderson, K., This GPT-powered robot chemist designs reactions and makes drugs — on its own, Nature 20 Dec. 2023  https://www.nature.com/articles/d41586-023-04073-4

• 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