Phase I publications

Phase I Publications

27. 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.

26. Dotson, J.J., van Dijk, L., Timmerman, J.C., Grosslight, S., Walroth, R.C., Gosselin, F., Püntener, K., Mack, K.A. and Sigman, M.S., 2022. Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands. J. Am. Chem. Soc. 2023, 145, 110–121.

25. 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. and Han, C., 2022. 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.

24. 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.

23. 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

22. 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. Engin. 2022, 7, 1276-1284

21. 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

20. 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. 2021

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

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.

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

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.

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.

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.

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.

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

11.  Gensch, T.; dos Passos 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

10. 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. Data-science driven autonomous process optimization. Comm. Chem. 2021, 4 112.

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.

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, 1856-865.

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

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

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

4. 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.

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.

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. Art. Int. 2020 34, 9024-9031.

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.