Phase I publications

Phase I Publications

28. 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-Driving 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

DOI of dataset(s): https://doi.org:/10.5281/zenodo.15652106

27. Hardy, M. A.; Cooke, J. H.; Feng, Z.; Noda, K.; Kerschgens, I.; Massey, L. A.; Tantillo, D. J.; Sarpong, R. Unified Synthesis of 2-Isocyanoallopupukeanane and 9-Isocyanopupukeanane through a "Contra-biosynthetic" Rearrangement. Angew. Chem. Int. Ed. 2024, 63, e202317348. https://doi.org/10.1002/anie.202317348

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

25. Dotson, J. J., van Dijk, L.; Timmerman, J. C.; Grosslight, S.; Walroth, R. C.; Gosselin, F.; Püntener, K.; Mack, K. A.; Sigman, M. S. Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands. J. Am. Chem. Soc. 2023, 145, 110–121. https://doi.org/10.1021/jacs.2c08513

24. Xu, J.; Grosslight, S.; Mack, K. A.; Nguyen, S. C.; Clagg, K.; Lim, N. K.; Timmerman, J. C.; Shen, J.; White, N. A.; Sirois, L. E.; Han, C.; Zhang, M.; Sigman, M. S.; Goesslin, F. Atroposelective Negishi Coupling Optimization Guided by Multivariate Linear Regression Analysis: Asymmetric Synthesis of KRAS G12C Covalent Inhibitor GDC-6036. J. Am. Chem. Soc. 2023, 145, 20955–20963. https://doi.org/10.1021/jacs.2c09917

23. Torres, J. A. G.; Lau, S. H.; Anchuri, P.; Stevens, J. M; Tabora, J. E; Li, J.; Borovika, A.; Adams, R. P; Doyle, A. G. A Multi-Objective Active Learning Platform and Web App for Reaction Optimization. J. Am. Chem. Soc. 2022, 144, 1999–2007. https://pubs.acs.org/doi/10.1021/jacs.2c08592

22. 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, 1115–1123. https://www.google.com/url?q=https://doi.org/10.1021/acs.oprd.1c00357&source=gmail-imap&ust=1647892041000000&usg=AOvVaw1-_3s3DLVEYf_NMQYJM5jI

21. Żurański, A. M.; Wang, J. Y.; Shields, B. J.; Doyle, A. G. Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules. React. Chem. Eng. 2022, 7, 1276–1284. https://pubs.rsc.org/en/content/articlelanding/2022/RE/D2RE00030J

20. Hardy, M. A.; Nan, B.; Wiest, O.; Sarpong, R. Strategic elements in computer-aided retrosynthesis: A case study of the pupukeanane natural products Tetrahedron 2022, 103, 132584. https://doi.org/10.1016/j.tet.2021.132584

19. Jones, K. E.; Park, B.; Doering, N. A.; Baik, M. H.; Sarpong, R. Rearrangements of the Chrysanthenol Core: Application to a Formal Synthesis of Xishacorene B. J. Am. Chem. Soc. 2021, 143, 20482–20490. https://doi.org/10.1021/jacs.1c10804

18. 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

17. 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, 7773–7780. https://doi.org/10.1021/acscatal.2c01970

16. 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

15 Saebi, M.; Nan, B.; Herr, J. E.; Wahlers, J.; Guo, Z.; Zurański, A. M.; Kogej, T.; Norrby, P. O.; Doyle, A. G.; Chawla, N. V.; Wiest, O., 2023. On the use of real-world datasets for reaction yield prediction. Chem. Sci. 2023, 14, 4997–5005. https://doi.org/10.1039/D2SC06041H

14. Guan, Y.; Sowndarya. S. S.; Gallegos, L. C.; St. John, P. C.; Paton, R. S., Real-Time Prediction of 1H and 13C Chemical Shifts with DFT Accuracy Using a 3D Graph Neural Network. Chem. Sci. 2021, 12, 12012–12026. https://doi.org/10.1039/D1SC03343C

13. 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

12. 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. http://science.org/doi/10.1126/science.abj4213

11. Gensch, T.; Gomes, G.; Friederich, P.; Peters, E.; Gaudin, T.; Pollice, R.; Jorner, K.; Nigam, A.; Lindner-D’Addario, M.; Sigman, M. S.; Aspuru-Guzik, A. A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis. J. Am. Chem. Soc. 2022, 144, 1205–1217. https://pubs.acs.org/doi/full/10.1021/jacs.1c09718

10. Christensen M.; Yunker L.; Adedeji F.; Häse F.; Roch L.; Gensch T.; Gomes G., Zepel T, Sigman M. S.; Aspuru-Guzik A, Hein J. Data-science driven autonomous process optimization. Data-science driven autonomous process optimization. Commun. Chem. 2021, 4, 112. https://www.google.com/url?q=https://doi.org/10.1038/s42004-021-00550-x&source=gmail-imap&ust=1628536346000000&usg=AOvVaw3T_qzBdv4Af3jRhva2cwTM

9. Kariofillis S.; Jiang S.; Żurański A.; Gandhi S.; Martinez Alvarado J.; Doyle A. G. 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, 1045–1055. https://pubs.acs.org/doi/10.1021/jacs.1c12203

8. Żurański, A. M.; Martinez Alvarado, J. I.; Shields, B. J.; Doyle, A. G. 2021. Predicting Reaction Yields via Supervised Learning. Acc. Chem. Res. 2021, 54, 1856–1865. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00770

7. Gallegos, L. C.; Luchini, G.; St John, P. C.; Kim, S.; Paton, R. S. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties Acc. Chem. Res. 2021, 54, 4, 827–836. https://pubs.acs.org/doi/10.1021/acs.accounts.0c00745

6. 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://doi.org/10.1038/s43586-021-00022-5

5. Guo, Z.; Zhang, C.; Yu, W.; Herr, J.; Wiest, O.; Chawla, N. V. Few-Shot Graph Learning for Molecular Property Prediction. Proc. TheWebConf2021 2021, 2559–2567. https://doi.org/10.1145/3442381.3450112

4. Shields, B. J.; Stevens, J.; Li, J.; Prarasram, M.; Damani, F.; Martinez Alvaro, J.; Janey, J.; Adams, R. P.; Doyle, A. G. Bayesian Reaction Optimization as A Tool for Chemical Synthesis. Nature 2021, 590, 89–96. https://doi.org/10.1038/s41586-021-03213-y

3. Luchini, G.; Alegre-Requena, J. V.; Funes-Ardoiz, I.; Paton, R. S. GoodVibes: Automated thermochemistry for heterogeneous computational chemistry data. F1000Research, 2020, 9, 291. https://doi.org/10.12688/f1000research.22758.1

2. Tang, P.; Jiang, M.; Xia, B. N.; Pitera. J. W.; Welser. J.; Chawla, N. V. Multi-label patent categorization with non-local attention-based graph convolutional network. Proc. AAAI Conf. Artif. Intell. 2020, 34, 9024–9031.34i05.6435. https://doi.org/10.1609/aaai.v34i05.6435

1. Guo, Z.; Yu, W.; Zhang, C.; Jiang, M.; Chawla, N. V. GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. Proc. 29th ACM Intl. Conf. Inf. Knowl. Manag. 2020, 435–443. https://doi.org/10.1145/3340531.3411981