Reaction Discovery & Development Optimization

People

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

  • 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

  • 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

  • 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