| Title: | QSPR models for predicting log P(liver) values for volatile organic compounds combining statistical methods and domain knowledge |
| Author(s): | Palomba D; Martinez MJ; Ponzoni I; Diaz MF; Vazquez GE; Soto AJ; |
| Address: | "Planta Piloto de Ingenieria Quimica-PLAPIQUI, CONICET-UNS, La Carrindanga km 7, Bahia Blanca 8000, Argentina" |
| DOI: | 10.3390/molecules171214937 |
| ISSN/ISBN: | 1420-3049 (Electronic) 1420-3049 (Linking) |
| 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 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 P(liver), 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 P(liver) 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" |
| Keywords: | "Animals Artificial Intelligence *Gases/chemistry/toxicity Humans Liver/drug effects *Models, Theoretical *Quantitative Structure-Activity Relationship Rats *Volatile Organic Compounds/chemistry/toxicity;" |
| Notes: | "MedlinePalomba, Damian Martinez, Maria J Ponzoni, Ignacio Diaz, Monica F Vazquez, Gustavo E Soto, Axel J eng Research Support, Non-U.S. Gov't Switzerland 2012/12/19 Molecules. 2012 Dec 17; 17(12):14937-53. doi: 10.3390/molecules171214937" |
