Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions

Abstract

A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper−bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 new bis(oxazoline) ligands that were synthesized and tested for stereoselectivity in the coppercatalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and unsupervised catalyst selection methods, including quantitative structure−selectivity relationship modeling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 new bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes and in some cases demonstrated enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure− selectivity relationship modeling approach.

Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

“…Selecting substrates from the different clusters in the drug map enables an unbiased selection of a diverse set of representative examples. Our strategy also complements previous unsupervised learning based substrate selection approaches which focused on quantum chemical modeling of the reactive center . We have demonstrated the applicability of the workflow on different substrate classes, namely, alkenes and (hetero)­aryl bromides, and utilized it to assess the scope of different chemical reactions.…”

“…Next, we featurized the drug molecules utilizing extended connectivity fingerprints (ECFP) . While quantum chemical descriptors have been shown to be effective in mapping molecular structures, they feature major disadvantages for the given application. Typically, they are highly problem- and structure-specific, lacking a general set of descriptors to describe the high structural diversity of the drug chemical space.…”

“…These filters can typically be obtained from the conventional scope of a reaction’s incompatibility toward specific functional groups or steric restrictions (Figure A). Complementary unbiased data-driven approaches such as the quantum chemically modeled substrate selection could also be utilized for identifying electronic and steric reactivity trends and defining filters.…”

“…In addition, the utilized principal component analysis method cannot identify complex global and local structural patterns. Recently, Doyle and co-workers reported an efficient method for the scope selection of aryl bromides based on their coverage of the chemical space . Their method selects the scope broadly to cover electronic and steric effects of substituents around the reaction center based on calculated quantum chemical descriptors.…”