Fuzzy QSARs for predicting logKoc of persistent organic pollutants

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors

CORAL software (http:/www.insilico.eu/coral) has been used to build up quantitative structure-biodegradation relationships (QSPR). The normalized degradation percentage has been used as the measure of biodegradation (for diverse organic compounds, n=445). Six random splits into sub-training, calibration, and test sets were examined. For each split the QSPR one-variable linear regression model based on the SMILES-based optimal descriptors has been built up. The average values of numbers of compounds and the correlation coefficients (r2) between experimental and calculated biodegradability values of these six models for the test sets are n=88.2±11.7 and r2=0.728±0.05. These six models were further tested against a set of chemicals (n=285) for which only categorical values (biodegradable or not) were available. Thus we also evaluated the use of the model as a classifier. The average values of the sensitivity, specificity, and accuracy were 0.811±0.019, 0.795±0.024, and 0.803±0.008, respectively.