Prediction of Molecular Lipophilicity for Aromatic Aldehydes to Tetrahymena Pyriformis Using QSPR Approach

Abstract

In this study, a predictive QSPR (quantitative structure–property relationship) model was developed using Dragon descriptors to estimate the lipophilicity (LogKow) of aromatic aldehydes. The model was constructed with a dataset of 77 compounds and utilized multiple linear regression analysis, along with the combination of the ordinary least square regression method and genetic algorithm-based variable subset selection. The resulting model exhibited a high correlation coefficient (R2) of 88.71% and a standard error of estimation (s) of 0.324 log unit, indicating its reliability. Further validation was performed on an independent test set of 23 compounds, demonstrating the model's effectiveness in predicting the lipophilicity of new aromatic aldehydes. This valuable information can aid in drug design and optimization efforts, potentially facilitating the development of novel pharmaceuticals.