Guiding Target Synthesis with Statistical Modeling Tools: A Case Study in Organocatalysis

Abstract

Practitioners are generally not willing to explore modern reactions where considerable synthetic effort is required to generate materials and the results are not certain. Organocatalysis exemplifies this, in which a broad set of enantioselective reactions have been successfully developed but further applications to include additional substrates are often not performed. Herein we demonstrate how statistical models can be utilized to accurately distinguish between different catalysts and reactions to guide the selection of efficient synthetic routes to obtain a target molecule.

Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity

“…9,10 Computational approaches help in identifying structural aspects pertinent to reactivity and inform the design of improved catalysts. [11][12][13][14][15][16] A strategy popularized by Sigman and co-workers is to correlate physical organic descriptors to experimental activity or selectivity outcomes via multivariate regression analysis. [17][18][19][20][21][22][23][24][25] not fully exploit the fragment-based nature of organocatalysts and has limited transferability because, when even small modifications on part of the catalyst are made, its entire structure must be re-optimized and the parameters collected.…”

“…9,10 Computational approaches help in identifying structural aspects pertinent to reactivity and inform the design of improved catalysts. 11–16 A strategy popularized by Sigman and co-workers is to correlate physical organic descriptors to experimental activity or selectivity outcomes via multivariate regression analysis. 17–25 When applied to organocatalysts, the descriptors are typically evaluated on the catalyst structure as a whole or, occasionally, from truncated versions of it.…”

Interrogating the thionium hydrogen bond as a noncovalent stereocontrolling interaction in chiral phosphate catalysis

“…those that generate one stereocenter). 58,59 For the purpose of forecasting this reaction, the sign of the ee (either -ve or +ve) represents one of two electrophile orientations which result in opposite enantiomers when catalysed by a (R)-CPA as shown by the product models displayed in Figure 7A. Since the model built on intermolecular systems is to be applied to predict the results of an intramolecular reaction, we suspected an adapted parameter set may be necessary to facilitate such a significant extrapolation reaction space.…”

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

“…The construction of comprehensive statistical models has emerged as a recent focus, and application to catalytic settings has demonstrated their value in predicting unique reaction outcomes. 3,[20][21][22][23] Considering this, and the substantial dynamic error associated with out-of-sample prediction platforms, 24 provided the impetus to apply our probabilistic modelling framework to this setting.…”