Automation High-Throughput
Laboratory automation utilizes a combination of robotics, hardware, and software planning to decrease manual effort from the researcher, particularly in high-throughput testing and screening. Further combining automation platforms with ML models can work towards closed-loop systems for chemical optimization and discovery.
Publications
Boiko, D.A., MacKnight, R., Kline, B. and Gomes, G., 2023. Autonomous chemical research with large language models. Nature, 624(7992), 570-578. doi: 10.1038/s41586-023-06792-0
Casetti, N., Alfonso-Ramos, J.E., Coley, C.W. and Stuyver, T., Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery. Chemistry Europ. J., 2023 p.e202301957. https://doi.org/10.1002/chem.202301957
Christensen M, Yunker L, Adedeji F, Häse F, Roch L, Gensch T, dos Passos Gomes G, Zepel T, Sigman M, Aspuru-Guzik A, Hein J. Data-science driven autonomous process optimization. Commun. Chem. 2021, 4 112. https://doi.org/10.1038/s42004-021-00550-x
Raghavan, P.; Haas, B.C.; Ruos, M.E.; Schleinitz, J.; Doyle, A.G.; Reisman, S.E.; Sigman, M.S.; Coley, C.W. Dataset Design for Building Models of Chemical Reactivity. ACS Cent. Sci 2023, 9, ASAP https://doi.org/10.1021/acscentsci.3c01163
Wang JY, Stevens JM, Kariofillis SK, Tom MJ, Golden DL, Li J, Tabora JE, Parasram M, Shields BJ, Primer DN, Hao B. Identifying general reaction conditions by bandit optimization. Nature. 2024, 626, 1025-1033 https://doi.org/10.1038/s41586-024-07021-y
Maloney MP, Coley CW, Genheden S, Carson N, Helquist P, Norrby PO, Wiest O. Negative Data in Data Sets for Machine Learning Training. Organic Letters. 2023, ;25, 2945-2947 https://pubs.acs.org/doi/10.1021/acs.joc.3c00844. Published in parallel in J. Org. Chem. 2023, 88, 5239–5241 https://doi.org/10.1021/acs.joc.3c00844
https://doi.org/10.1021/acs.orglett.3c01282Żurański, A.M., Martinez Alvarado, J.I., Shields, B.J. and Doyle, A.G. 2021. Predicting Reaction Yields via Supervised Learning. Acc. Chem. Res. 2021, 54, 1856-865. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00770
Williams, W.L.; Zeng, L.; Gensch, T.; Sigman, M.S.; Doyle, A.G.; Anslyn, E. V. The Evolution of Data-Driven Modeling in Organic Chemistry. ACS Cent. Sci. 2021, 7, 1622-1637. https://doi.org/10.1021/acscentsci.1c00535