Abstract
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.
Highlights
The soil sorption coefficient (Koc) describes the biodegradation and pollution impact of organic pesticides [1] when these compounds interact with the organic matter of soils and sediments either on surface, ground or drinking water [2]
We have succeeded in establishing structure-property relationships for the soil sorption coefficient, a useful parameter related to sorption processes determining the environmental fate, distribution and persistence of chemicals
The chemical domain explored includes a heterogeneous set of 643 organic non-ionic compounds, having a Koc range of more than six log units
Summary
The soil sorption coefficient (Koc) describes the biodegradation and pollution impact of organic pesticides [1] when these compounds interact with the organic matter of soils and sediments either on surface, ground or drinking water [2]. Predicting the soil sorption coefficient for a wide number of chemical structures is very convenient in risk assessment [3]. In the realm of the Quantitative Structure-Property Relationships (QSPR) theory [4,5,6], an experimental property of a chemical compound, i.e., Koc, can be predicted through the knowledge of its chemical structure. The structure is quantified by means of a set of suitable molecular descriptors, in other words, numerical quantities carrying specific information on the constitutional, topological, geometrical, hydrophobic, and/or electronic aspects [7,8,9]. A set of descriptors is statistically correlated with the experimental property, resulting in a mathematical model that can be used with find out useful parallelisms
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