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

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.

Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality.

Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization of any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated with physical organic methods to identify the origins of selectivity.

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings.

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.

Synthesis of tetraphosphine macrocycles using copper(i) templates.

The synthesis of phosphine macrocycles is a relatively underdeveloped area and no standard synthetic routes have emerged. Accordingly, two general synthetic routes to tetradentate phosphine macrocycles were investigated. Both routes use Cu(i) ions as template ions because, unlike other metals such as Pd(ii) and Pt(ii), the Cu(i) ions can be removed from the macrocyclic complex without degrading the macrocycle ligand. The first route involves the coupling of two bidentate secondary phosphines bonded to Cu(i) using 1,3-dibromopropane or 1,4-dibromobutane. Using this route, tetradentate phosphine macrocycles with either -(CH2)3OCH3 or Ph groups bonded to the P atoms were synthesized. Macrocycle phosphines containing the -(CH2)3OCH3 groups were investigated for their potential water-solubility, but experiments showed these phosphines were not water soluble. The second synthetic route involved the alkylation of an open-chain, mixed tertiary-secondary, tetradentate phosphine coordinated to Cu(i). Following formation of the macrocyclic ligand, the Cu(i) template was removed by reaction with aqueous KCN to yield the free macrocyclic phosphine. This route was demonstrated for the preparation of the macrocyclic phosphine ligand 1,5,9,13-tetraphenyl-1,5,9,13-tetraphosphacycloheptadecane. Following demetallation, this macrocyclic ligand was coordinated to Fe(ii) and Co(ii) to form the corresponding macrocyclic phosphine complexes.