Quantum Molecular Interaction Field Models of Substrate Enantioselection in Asymmetric Processes

Abstract

Computational models correlating substrate structure to enantioselection with asymmetric catalysts using the QMQSAR program are described. In addition to rapidly providing predictions that could be used to facilitate the screening of catalysts for novel substrates, the QMQSAR program identifies the portions of the substrate that most directly influence the enantioselectivity. The lack underlying relationship between all the substrates in one case, requires two quantitative structure selectivity relationships (QSSR) models to describe all of the experimental results.

Figure 1

Cross validation results of substrates from entries 1–11 (Table 1): Plot of predictions from leave-one-out models constructed from the 10 remaining substrates (y = 0.85x + 0.30, r²_LOO = 0.67, CC = 0.82)

Figure 2

Superposition of the EPF points from the 11 leave-one-out models around the 11 aldehyde substrates listed in Table 1 (blue = positive EFP value correlates to higher ee, red = positive EFP value correlates to lower ee).

Figure 3

Cross validation results of substrates from entries 1–10 (Table 2): Plot of predictions from leave-one-out models constructed from the 9 remaining substrates (y = 0.82x + 0.01, r²_LOO = 0.61, CC_LOO = 0.77)

Figure 4

Cross validation results of substrates from entries 11–20 (Table 2): Plot of predictions from leave-one-out models constructed from the 9 remaining substrates (y = 0.89x + 0.38, r²_LOO = 0.61, CC_LOO = 0.78)