Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts

Abstract

Conspectus Catalyst design in enantioselective catalysis has historically been driven by empiricism. In this endeavor, experimentalists attempt to qualitatively identify trends in structure that lead to a desired catalyst function. In this body of work, we lay the groundwork for an improved, alternative workflow that uses quantitative methods to inform decision making at every step of the process. At the outset, we define a library of synthetically accessible permutations of a catalyst scaffold with the philosophy that the library contains every potential catalyst we are willing to make. To represent these chiral molecules, we have developed general 3D representations, which can be calculated for tens of thousands of structures. This defines the total chemical space of a given catalyst scaffold; it is constructed on the basis of catalyst structure only without regard to a specific reaction or mechanism. As such, any algorithmic subset selection method, which is unsupervised (i.e., only considers catalyst structure), should provide an ideal initial screening set for any new reaction that can be catalyzed by that scaffold. Notably, because this design strategy, the same set of catalysts can be used for any reaction that can be catalyzed with that parent catalyst scaffold. These are tested experimentally, and statistical learning tools can be used to create a model relating catalyst structure to catalyst function. Further, this model can be used to predict the performance of each catalyst candidate in the greater database of virtual catalyst candidates. In this way, it is possible estimate the performance of tens of thousands of catalysts by experimentally testing a smaller subset. Using error assessment metrics, it is possible to understand the confidence in new predictions. An experimentalist using this tool can balance the predicted results (reward) with the prediction confidence (risk) when deciding which catalysts to synthesize next in an optimization campaign. These catalysts are synthesized and tested experimentally. At this stage, either the optimization is a success or the predicted values were incorrect and further optimization is required. In the case of the latter, the information can be fed back into the statistical learning model to refine the model, and this iterative process can be used to determine the optimal catalyst. In this body of work, we not only establish this workflow but quantitatively establish how best to execute each step. Herein, we evaluate several 3D molecular representations to determine how best to represent molecules. Several selection protocols are examined to best decide which set of molecules can be used to represent the library of interest. In addition, the number of reactions needed to make accurate, statistical learning models is evaluated. Taken together these components establish a tool ready to progress from the development stage to the utility stage. As such, current research endeavors focus on applying these tools to optimize new reactions.

Machine intelligence for chemical reaction space

“…The relevance of chemically meaningful features was shown by attempting extrapolative predictions using randomized labels, which led to worsened performance. 155 Huang et al 156 focused on improving the featurization of the Spectral London Axilrod-Teller-Muto (SLATM) structure-based representation in order to develop and improve the performance of kernel ridge regression models for the enantioselectivity prediction. They introduced reaction-based representations and exploited metric learning and supervised feature selection techniques to filter the information contained in the molecular representations.…”

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

“…6 Alternatively, 3D descriptors represent general strucutural information, and thus models on 3D structures may identify correlations without requiring knowledge of the detailed reaction mechanism. 10 These methods have already been applied in asymmetric catalysis, using calculated 3D structures with grid-based binary feature vectors, such as Comparative Molecular Field Analysis (CoMFA). [11][12][13][14][15] The Denmark group reported a modification of grid-based methods with non-binary features based on distribution of conformations to account for their flexibility, and demonstrated that the model can predict higher selectivity even in the absence of such examples in the training dataset.…”

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

“…1,2 However, the complex nature of modern reactions, and the subtle variation in reaction conditions between similar substrates, can make the selection challenging. 3,4 Predictive models like multivariate linear regression (MLR) have been advanced to address this issue [5][6][7][8][9][10] with numerous successful applications in selective catalyst 11,12 and reaction 13 development having been recorded. However, such statistical models need to be both reliable and interpretable to be widely accepted by practitioners.…”