ArticleMolecules (Basel, Switzerland)2012
QSPR models for predicting log P(liver) values for volatile organic compounds combining statistical methods and domain knowledge.
Article in Molecules (Basel, Switzerland), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour.PloS one · 2023Article
- Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity.Journal of chemical information and modeling · 2022Article
- Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.International journal of molecular sciences · 2020Article
- MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.BioMed research international · 2019Article
- Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.Scientific reports · 2017Article
- Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).PloS one · 2017Article
- Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.Journal of cheminformatics · 2015Article
- Navigating the chemical space of dipeptidyl peptidase-4 inhibitors.Drug design, development and therapy · 2015Article
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6 authors.
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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 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.
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