Molecules 2012, 17(12), 14937-14953; doi:10.3390/molecules171214937
Article

QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge

Damián Palomba 1,2,† email, María J. Martínez 2,† email, Ignacio Ponzoni 1,2 email, Mónica F. Díaz 1,2 email, Gustavo E. Vazquez 2 email and Axel J. Soto 3,* email
1 Planta Piloto de Ingeniería Química (PLAPIQUI) CONICET-UNS, La Carrindanga km.7, Bahía Blanca, 8000, Argentina 2 Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), DCIC, UNS, Av. Alem 1250, Bahía Blanca, 8000, Argentina 3 Faculty of Computer Science, Dalhousie University, 6050 University Av., PO BOX 15000, Halifax, NS B3H 4R2, Canada † These authors contributed equally to this work.
* Author to whom correspondence should be addressed.
Received: 10 September 2012; in revised form: 12 December 2012 / Accepted: 13 December 2012 / Published: 17 December 2012
(This article belongs to the Special Issue QSAR and Its Applications)
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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.
Keywords: log Pliver; VOCs; machine learning; QSPR

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MDPI and ACS Style

Palomba, D.; Martínez, M.J.; Ponzoni, I.; Díaz, M.F.; Vazquez, G.E.; Soto, A.J. QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge. Molecules 2012, 17, 14937-14953.

AMA Style

Palomba D, Martínez MJ, Ponzoni I, Díaz MF, Vazquez GE, Soto AJ. QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge. Molecules. 2012; 17(12):14937-14953.

Chicago/Turabian Style

Palomba, Damián; Martínez, María J.; Ponzoni, Ignacio; Díaz, Mónica F.; Vazquez, Gustavo E.; Soto, Axel J. 2012. "QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge." Molecules 17, no. 12: 14937-14953.

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