Abstract
Volatile organic compounds (VOCs) are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver) for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR) models for the prediction of log Pliver, where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log Pliver models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property.
Highlights
Volatile organic compounds (VOCs) are emitted as gases from certain solids or liquids
A critical step in the development of quantitative structure-property relationship (QSPR) models is the computation of the molecular descriptors
As a first step a machine learning approach based on a cross-fold validation with in-fold feature selection was applied [20]
Summary
Volatile organic compounds (VOCs) are emitted as gases from certain solids or liquids. Varnishes, and wax all contain organic solvents, as do many cleaning, disinfecting, cosmetic, degreasing, and hobby products. All of these products may release organic compounds while they are used, and, to some degree, when they are stored. Woodruff et al [3] described the need for better public health policies on chemicals released into our environment. They proposed modernizing approaches to assessing health risk and remarked the importance of scientific understanding of the relationship between pollutant exposure and adverse health effects
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