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Automation High-Throughput

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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

  • Gandhi, S.S., Brown, G.Z., Aikonen, S., Compton, J.S., Neves, P., Martinez Alvarado, J.I., Strambeanu, I.I., Leonard, K.A., Doyle, A.G. Data Science-Drivin Discovery of Optimal conditions and a condition-Selection Model for the Chan-Lam Coupling of Primary Sulfonamindes. ACS Catal. 2025. 15, 2292-2304. https://doi.org/10.1021/acscatal.4c07972?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-as

  • 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
















  • Schleinitz, J., Carreteri-Cerdan, A., Gurajapu, A., Harnik, Y., Lee, G., pandey, A., Milo, A., Reisman, S.E. Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates. J. Am. Chem. Soc. 2025. https://pubs.acs.org/action/showCitFormats?doi=10.1021/jacs.4c15902&ref=pdf

  • Feng, K., Raguram, E.R., Howard, J.R., Peters, E., Liu, C., Sigman, M.S.; Buchwald, S.L., Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines. J. Am. Chem. Soc. 2024, 146 ASAP . https://doi.org/10.1021/jacs.4c09667

  • 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.orglett.3c01282

  • Żurański, A.M., Martinez Alvarado, J.I., Shields, B.J. and Doyle, A.G..  Predicting Reaction Yields via Supervised Learning. Acc. Chem. Res. 2021, 54, 1856-865. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00770

  • MacKnight, R.; Boiko, D. A.; Regio J. E.; Gallegos, L.C.; Neukomm, T.A, Gomes, G. Rethinking chemical research in the age of large language models Nature Comp. Sci. 2025, doi.org/10.1038/s43588-025-00811-y

  • 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

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