Development and Molecular Understanding of a Pd‐Catalyzed Cyanation of Aryl Boronic Acids Enabled by High‐Throughput Experimentation and Data Analysis

Abstract

A synthetic method for the palladium-catalyzed cyanation of aryl boronic acids using bench stable and nontoxic N-cyanosuccinimide has been developed. High-throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ-donation, π-acidity and sterics are key drivers that govern yield. Categorization into three ligand groups -monophosphines, bisphosphines and miscellaneous -was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ-donating, weak π-accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron-withdrawing substituents. This work outlines the development of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross-coupling catalysts.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

“…The kraken tool may enable informed catalyst design based on organophosphorus ligands, facilitate the optimization of reaction process parameters, inspire new ligand choices, and promote the synthesis of new organophosphorus compounds. The database and tools reported herein are currently being applied to enhance reaction optimization − and mechanistic workflows . The open-source nature of our codes, as well as the open database, is designed to be extended by others, and we welcome further contributions by the community.…”

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

“…For the Hayashi–Heck reaction, regioselectivity (i.e., formation of olefin 4 vs 5 ) was correlated to two parameters, aniso31 P NMR and P–C σ* occupancy, the computed anisotropic phosphorus NMR shielding and the occupancy of the σ* orbitals of the P–C bonds, respectively (Figure B) . While the latter term has been postulated to correlate with the metal-to-ligand backbonding interactions, the former was more difficult to interpret. However, more detailed analysis suggests that aniso31 P NMR is likely a hybrid parameter reflecting both distal sterics and phosphorus electron richness (see the Supporting Information for further discussion).…”

Mechanistic Inference from Statistical Models at Different Data-Size Regimes

“…Kraken has been used as the initial step in optimization campaigns, guiding the selection of ligands from different regions of the chemical space. These ligands have been evaluated using high-throughput experimentation (HTE) , and multiobjective optimization strategies . More recently, the Doyle and Sigman groups have demonstrated how the use of data available in Kraken can also drive new mechanistic discoveries .…”

“…Moreover, a recent review by Norrby and co-workers provides an excellent computational chemistry assessment of how the mechanistic study of organic reactivity has been affected by machine learning . Herein, we also set mechanistic inference as our mainstay; however, our specific aim is to demonstrate through selected examples that a careful experimental design not only enables efficient reaction optimization but can concurrently generate uniquely suitable data sets for mechanistic investigation. − …”