A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Abstract

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

Data Science Meets Physical Organic Chemistry

“…This mindset enables the identification of subtle trends within the data, even when a particular result may be unexpected from a chemical intuition standpoint, which can guide further screening, hypothesis development, and future optimization campaigns. The expansion and distribution of databases of physical organic features will help to increase the accessibility of the data science workflow to chemists in a variety of fields. , It should be noted that the incorporation of data science principles to project design goes hand-in-hand with modern advances in automation that streamline the data collection process. , …”

Data-science driven autonomous process optimization

“…SI-5 through SI-10, Supplementary Data 1 through 2, and Supplementary Movie 1 through 2. The computed molecular features utilized in this study have been made publicly available at https:// kraken.cs.toronto.edu/dashboard 45 .…”

“…We employed a Chemspeed SWING robotic system for the experimental execution of parallel reaction loops in batch and employed the Phoenics and Gryffin algorithms for the proposal of parallel combinations continuous and categorical process parameter selections. Recognizing the impact of phosphine selection on the optimization outcome, we employed a variety of categorical parameter selection strategies, including chemical intuition and computed molecular descriptor clustering of 365 commercially available phosphines 45 . Here, we discuss the advantages and limitations of each phosphine selection strategy and their impacts on this challenging optimization problem.…”

A reactivity map for oxidative addition enables quantitative predictions for multiple catalytic reaction classes

“…All too often these efforts fail, impeding access to potentially promising new medicines and materials. Emerging approaches in reactivity prediction that combine highthroughput experimentation [5][6][7][8] with molecular descriptor sets [9][10][11] and multivariate statistical analysis including machine learning [12][13][14][15][16] can accelerate this process and increase success rates; however, the predictions generated by these approaches are often limited to the specific reaction under investigation (Figure 1A). Developing and refining the next generation of organic chemistry tools, including computer-aided synthesis design, automated reaction optimization, and predictive algorithms, 17 requires the development of general and quantitative frameworks linking molecular structure to reactivity for many different reactants and catalysts.…”