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

Molecules

Damián Palomba¹  María J. Martínez¹  Ignacio Ponzoni¹  Mónica F. Díaz¹  Gustavo E. Vazquez¹ 

[1] ¹Planta Piloto de Ingeniería Química (PLAPIQUI) CONICET-UNS, La Carrindanga km.7, Bahía Blanca, 8000, Argentina

关键词: log P_liver;    VOCs;    machine learning;    QSPR;   

DOI  :  10.3390/molecules171214937

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.