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

Abstract

AbstractChromatographic retention factor log kIAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight — log MW; molar volume — VM; 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 — EHOMO; energy of the lowest unoccupied orbital — ELUMO; dipole moment — DM; polarizability — α) 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 Koc. It was established that log kIAM 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 kIAM 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 Koc 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 (R2 ≥ 0.80, n = 50) which proves the models’ reliability.