In the present investigation, quantitative structure–property relationship (QSPR) modeling was carried out on 48 aliphatic esters to develop a robust model for the prediction of thermodynamic properties such as the enthalpy of vaporization at standard condition (∆H°vap kJ mol−1) and normal temperature of boiling points (Tbp° K). Multiple linear regression (MLR) and backward (BW) stepwise regression methods were used to select the descriptors derived from the Chemicalize program to give the QSPR models. These models were used to delineate the important descriptors responsible for the properties of the aliphatic esters. The multicollinearity and autocorrelation properties of the descriptors used in the models were tested by calculating the variance inflation factor, Pearson correlation coefficient, and the Durbin–Watson statistics. Leave‐one‐out cross‐validation, leave‐group (fivefold)‐out, and external validation criteria (Q2F1, Q2F2, Q2F3, CCC, R2m) were proposed to verify the predictive performance of QSPR models derived by BW‐MLR analysis. The predictive ability of the models was found to be satisfactory. Thus, QSPR models derived from this study may be helpful for modeling and designing some new aliphatic esters and predicting their properties.

Aim and Objective: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure–Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors. Materials and Methods: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities. Results: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263. Conclusion: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.