Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches.

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K with R of 0.825. The values of statistical parameters including R, Q, RMSE and RMSE for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.