The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.
Bibliographical noteFunding Information:
Financial support was provided by the National Institutes of Health (1R35GM119457) and the Alfred P. Sloan Foundation (I.A.T. is a 2017 Sloan Fellow). Instrumentation for the University of Minnesota Chemistry NMR facility was supported from a grant through the National Institutes of Health (S10OD011952). X-ray diffraction experiments were performed with a diffractometer purchased through a grant from the NSF/MRI (1229400) and the University of Minnesota. We acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota and the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-05CH11231, for providing resources that contributed to the results reported within this paper.
© 2020 American Chemical Society. All rights reserved.
- catalyst prediction
- iterative supervised principal component analysis