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Reaction Discovery & Development Optimization

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Reaction discovery identifies new tools for organic chemists to access novel scaffolds/molecules. It is often difficult, time-consuming, expensive, and wasteful to identify hits and optimize reactions. In C-CAS, we strive to use and develop computational and ML tools to expedite the process of reaction optimization.

Publications

  • Gensch T, dos Passos Gomes G, Friederich P, Peters E, Gaudin T, Pollice R, et al. A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis. J. Am. Chem. Soc. 2022, 144 ASAP  https://pubs.acs.org/doi/full/10.1021/jacs.1c09718

  • Andrzej M. Żurański, Shivaani S. Gandhi, and Abigail G. Doyle. A machine learning approach to model interaction effects: development and application to alcohol deoxyfluorination. J. Am. Chem. Soc. 2023, 145,14,7898-7909. https://doi.org/10.1021/jacs.2c13093

  • Torres, Jose Antonio Garrido; Lau, Sii Hong; Anchuri, Pranay; Stevens, Jason M; Tabora, Jose E; Li, Jun; Borovika, Alina; Adams, Ryan P; Doyle, Abigail G. A Multi-Objective Active Learning Platform and Web App for Reaction Optimization. J. Am. Chem. Soc. 2022, 144,43,19999-20007. https://pubs.acs.org/doi/10.1021/jacs.2c08592

  • Shen, Y., Borowski, J., Hardy, M., Sarpong, R. Doyle, A., Cernak, T. Automation and computer-assisted planning for chemical synthesis. Nat Rev Methods Primers, 2021, 23, 1.    https://www.nature.com/articles/s43586-021-00022-5

  • 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

  • Shields, B.J. ; Stevens, J.; Li, J.; Prarasram, M.; Damani, F.; Martinez Alvaro, J., Janey, J. Adams, R.P., Doyle, A. Bayesian Reaction Optimization as A Tool for Chemical Synthesis. Nature 2021, 590, 89-96.  https://www.nature.com/articles/s41586-021-03213-y

  • Ortiz, K.; Dotson, J.; Robinson, D. J.; Sigman, M. S.; Karimov, R. R. “Catalyst-controlled enantioselective and regiodivergent addition of aryl boron nucleophiles to N-alkyl nicotinate salts,” J. Am. Chem. Soc. 2023, 145, 21,11781-11788. https://doi.org/10.1021/jacs.3c03048

  • Matthews, A.D., Peters, E., Debenham, J.S., Gao, Q., Nyamiaka, M.D., Pan, J., Zhang, L.K., Dreher, S.D., Krska, S.W., Sigman, M.S. and Uehling, M.R., 2023. Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science. ACS Catalysis, 13(24), 16195-16206. doi https://doi.org/10.1021/acscatal.3c04566

  • van Dijk, Lucy, Brittany C. Haas, Ngiap-Kie Lim, Kyle Clagg, Jordan J. Dotson, Sean M. Treacy, Katarzyna A. Piechowicz et al. "Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides." J. Am. Chem. Soc. 2023, 145 ASAP. https://doi.org/10.1021/jacs.3c06674

  • Romer, N.P., Min, D.S., Wang, J.Y., Walroth, R.C., Mack, K.A., Sirois, L.E., Gosselin, F., Zell, D., Doyle, A.G. and Sigman, M.S., 2024. Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes. ACS Catalysis, 14, pp.4699-4708

  • 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
















  • Gensch, T.; Smith, S.R; Colacot, T.J.; Timsina, Y.; Xu, G.; Glasspoole, B.W.; Sigman, M.S, Design and Application of a Screening Set for Monophosphine Ligands in Metal Catalysis. ACS Catal. 2022. 12, 13, 7773-7780.  https://doi.org/10.1021/acscatal.2c01970

  • Silva, J. D. J.;  Bartalucci, N.;  Jelier, B.;  Grosslight, S.;  Gensch, T.;  Schünemann, C.;  Müller, B.;  Kamer, P. C.;  Copéret, C.; Sigman, M. S., Development and Molecular Understanding of a Pd-catalyzed Cyanation of Aryl Boronic Acids Enabled by High-Throughput Experimentation and Data Analysis. Helv. Chim. Acta 2021 e2100200. https://doi.org/10.1002/hlca.202100200

  • 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

  • Crawford, J.M.; Gensch, T.; Sigman, M.S.; Elward, J.M.; Steves, J.E.  Impact of Phosphine Featurization Methods in Process Development. Org. Proc. Res. Dev. 2022, 26, 4, 1115-1123  https://doi.org/10.1021/acs.oprd.1c00357

  • Newman-Stonebraker, Samuel; Smith, Sleight; Borowski, Julia; Peters, Ellyn; Gensch, Tobias; Johnson, Heather; Sigman, Matthew; Doyle, Abigail. Linking Mechanistic Analysis of Catalytic Reactivity Cliffs to Ligand Classification. ChemRxiv, May12, 2021. https://doi.org/10.26434/chemrxiv.14388557.v1

  • Saebi, M.;  Nan, B.;  Herr, J.;  Wahlers, J.;  Guo, Z.; Zuranski, A. M.;  Kegej, T.;  Norrby, P.-O.;  Doyle, A. G.;  Wiest, O.; Chawla, N., Wiest, O. On the Use of Real-World Data Sets for Reaction Yield Prediction.  Chem. Sci., 2023, 14, 4997-5005.  https://doi.org/10.1039/D2SC06041H

  • Żurański, 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

  • Wang, J. Y.; Stevens, J. M.; Kariofillis, S. K.; Tom, M.-J.; Li, J.; Tabora, J. E.; Parasram, M.; Shields, B.; Primer, D.; Hao, B.; Del Valle, D.; DiSomma, S.; Furman, A.; Zipp, G. G.; Melnikov, S.; Paulson, J.; Doyle, A. Reinforcement learning prioritizes general applicability in reaction optimization.  ChemRxiv 2023 10.26434/chemrxiv-2023-dcg9d

  • Zell D; Kingston C; Jermaks J; Smith S.R.; Seeger N; Wassmer J; Sirois, L.E.; Han, C.; Zhang, H.; Sigman, M.S.; Gossling, F., Stereoconvergent and -divergent Synthesis of Tetrasubstituted Alkenes by Nickel-Catalyzed Cross-Couplings. J. Am. Chem. Soc. 2021, 143, 45,19078 -19090. https://doi.org/10.1021/jacs.1c08399

  • Liu, Zhen, Yurii S. Moroz, and Olexandr Isayev. "The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions." Chem.  Sci. 2023, 14, 10835-10846.  doi https://doi.org/10.1039/D3SC03902A

  • 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

  • Newman-Stonebraker, S. H.;  Smith, S. R.;  Borowski, J. E.;  Peters, E.;  Gensch, T.;  Johnson, H. C.;  Sigman, M. S.; Doyle, A. G., Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis. Science 2021, 374, 301-308   science.org/doi/10.1126/science.abj4213

  • Kariofillis S, Jiang S, Żurański A, Gandhi S, Martinez Alvarado J, Doyle A. Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources. J. Am. Chem. Soc. 2022, 144 ASAP . https://pubs.acs.org/doi/10.1021/jacs.1c12203

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