Chemoinformatics

[428] ChemScraper: leveraging PDF graphics instructions for molecular diagram parsing (with A. K. Shah, B. Amador, A. Dey, M. Creekmore, B. Ocampo, R. Zanibbi) IJDAR. 2024, 27, 394-414.

[425] Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation (with M. D. Burke, Y. Diao, J. Han, R. Switzky, H. Zhao) AI Magazine. 2024, 1-7. 

[424] Machine Learning to Develop Peptide Catalysts, Limitations, and Opportunities (with T. Schnitzer M. Schnurr, A. F. Zahrt, N. Sakhaee, H. Wennemers*) ACS Cent. Sci. 2024, 10, 367-373.
[423] Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions (with C. L. Olen, A. F. Zahrt, S. W. Reilly, D. Schultz, K. Emerson, D. Candito, X. Wang, N. A. Strotman*) ACS Catalysis. 2024, 14, 2642-2655.

[421] A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings (with N. I. Rinehart, R. K. Saunthwal, J. Wellauer, A. F. Zahrt, L. Schlemper, A. S. Shved, R. Bigler, S. Fantasia) Science 2023, 381, 965-972.

[420] High-Level Data Fusion Enables the Chemoinformatically Guided Discovery of Chiral Disulfonimide Catalysts for Atropselective Iodination of 2‑Amino-6-arylpyridines (with B. Rose, J. C. Timmerman, S. A. Bawel, S. Chin, H. Zhang) J. Am. Chem. 2022, 144, 22950-22964.

[418] Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts (with K.W. Lexa, K.M. Belyk, J. Henle, B. Xiang, R.P. Sheridan, R.T. Ruck, and E.C. Sherer) J. Org. Process Res. Dev. 2022, 26, 670-682.

[411] Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality (with N. I. Rinehart and A. F. Zhart) Chimia. 2021, 75, 592-597.

[410] Dreams, False Starts, Dead Ends and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts (with N. I. Rinehart, A. F. Zahrt, and J. J. Henle) Acc. Chem. Res. 2021, 54, 2041-2054.

[409] A Conformer-Dependent, Quantitative Quadrant Model (with A. F. Zahrt and N. I. Rinehart) Chem. Eur. J. 2021, 2021, 2343.

[408] Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next (with A. F. Zahrt, B. T. Rose, W. T. Darrow, and J. J. Henle) React. Chem. Eng. 2021, 6, 694-708.

[405] Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets (with A. F. Zahrt, J. J. Henle) ACS Comb. Sci. 2020. 22, 586-591.

[401] Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis (with J. J. Henle,  A. F. Zahrt, B. T. Rose, W. T. Darrow, Y. Wang) J. Am. Chem. Soc. 2020, 142, 11578−11592.

[398] Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future (With Andrew F. Zahrt, Soumitra V. Athavale) Chem. Rev. 2020, 120, 1620-1689

[386] Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis (with A. F. Zahrt) Tetrahedron 2019, 75, 1841-1851.

[385] Prediction of Higher Selectivity Catalysts by Computer Driven Workflow and Machine Learning (with A. F. Zahrt, J. J. Henle, B. T. Rose, Y. Wang, W. T. Darrow) Science 2019, 363, 247.