Data Science Meets Physical Organic Chemistry

Abstract

Metrics & MoreArticle Recommendations CONSPECTUS: At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction.While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin−Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔG ⧧ = −RT ln[(S)/(R)], where (S) and (R) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way.Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design.In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction continued...

Stereoconvergent and -divergent Synthesis of Tetrasubstituted Alkenes by Nickel-Catalyzed Cross-Couplings

“…At the outset of the investigation, preliminary experimental data indicated that monophosphine ligands effectively promoted a nickel(0) cross-coupling of enol tosylate 1a and pinacol boronate 2a to access representative tetrasubstituted alkene diastereomers 3aa (Figure A, see Supporting Information for details). To further streamline efforts toward developing a ligand-controlled diastereoconvergent transformation, a data-rich optimization approach was envisaged wherein chemical space analysis would guide high throughput experimentation (HTE). , The kraken organophosphorus­(III) descriptor library, which contains >190 conformationally relevant molecular descriptors for 1558 monodentate phosphorus ligands, was employed for this purpose . The dimensionality of the descriptors for commercially available phosphine ligands within the library was reduced using principal component analysis (PCA) .…”

Working at the interfaces of data science and synthetic electrochemistry

“…While single and multivariate linear regressions are powerful tools for understanding and correlating experimental data [ 11 ], these approaches tend to only represent gradients of chemical reactivity but do not describe the implicit complex reaction surface. Non-parametric algorithms are typically invoked in modeling complex relationships and have seen increasing use in electrochemistry [ 7 , 10 , 30 – 32 ].…”

Data-driven multi-objective optimization tactics for catalytic asymmetric reactions

“…An additional curation step was found necessary to produce statistically robust linear models based on the hypothesis that disparate ligand types may impart selectivity through unique mechanisms. 42 As the training set was diverse, interrogation of the chemical space revealed a group of ligands which occupied a position in chemical space remote from the remainder of the ligands evaluated (see SI Fig. S14).…”