Journal
MOLECULES
Volume 17, Issue 12, Pages 14937-14953Publisher
MDPI
DOI: 10.3390/molecules171214937
Keywords
log P-liver; VOCs; machine learning; QSPR
Funding
- Universidad Nacional del Sur [PGI 24/ZN15, PGI 24/ZN16]
- CONICET - National Research Council of Argentina [PIP112-2009-0100322]
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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 P-liver) 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.
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