Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Chromatographic retention factor log k(IAM) obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight - log M-W; molar volume - V-M; polar surface area - PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds - FRB; H-bond donor count - HD; H-bond acceptor count - HA; energy of the highest occupied molecular orbital - E-HOMO; energy of the lowest unoccupied orbital - E-LUMO; dipole moment - DM; polarizability - alpha) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes' soil-water partition coefficient normalized to organic carbon log K-oc. It was established that log k(IAM) obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log k(IAM) and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log K-oc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R-2 >= 0.80, n = 50) which proves the models' reliability.