Intelligent Consensus Predictions of the Retention Index of Flavor and Fragrance Compounds Using 2D Descriptors

Abstract

The demand for novel flavors and fragrance (F&F) compounds has increased, highlighting the need for a systematic design approach. Currently, the F&F industry relies heavily on experimental approaches without considering the potential consequences of altering the features that contribute to the fragrance of the compound. In silico approaches have great potential to identify the necessary features for creating novel F&F compounds. In the present study, Quantitative Structure–Property Relationship (QSPR) models were developed using 1208 compounds and simple 2D descriptors, focusing on the RI (retention index) as the endpoint to predict the olfactory properties of molecules. Feature selection was initially carried out by multi-layered stepwise regression followed by feature thinning using the Genetic Algorithm (GA) and optimal feature combination selection using the BSS (best subset selection) method. Final models were developed using the Partial Least Squares (PLS) method. Additionally, internal and external validation of the models was performed using different validation metrics suggesting that the developed models are reliable, predictive, reproducible, and robust. To enhance the external prediction of the developed models, an Intelligent Consensus Prediction (ICP) method was employed and CM3 (consensus model 3) (best selection of predictions (compound-wise) from individual models) was found to provide the best predictivity. The modeling descriptors suggested that the hydrophobicity, high molecular weight, aromaticity, and presence of large-size fragments (high percentage of carbon) enhance the RI values. Conversely, polarity and hydrophilicity decrease the RI values. This study can be used to optimize the stationary phase according to the flavor and fragrance compounds to obtain the desired retention index (RI values).

Graphical abstract