Abstract
In this work, we present Quantitative Structure-Activity Relationship (QSAR) classification models for characterization of molecules affinity to blood or liver for volatile organic compounds (VOCs), using information provided from log P liver measures for VOCs. The models are computed from a dataset of 122 molecules. As a first phase, alternative subsets of relevant molecular descriptors related to the target property are selected by using feature selection methods and visual analytics techniques. From these subsets, several QSAR models are inferred by different machine learning methods. These models allow classifying a new compound as a molecule with affinity to blood, to the liver or equal affinity to both. The model with the highest performance correctly classifies 72.13% of VOCs and has an average receiver operating characteristic area equal to 0.83. As a conclusion, this QSAR model can predict the medium affinity of a VOC, which can help in the development of physiologically based pharmacokinetic computational models required in e-health.
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References
Buist, H.E., de Wit-Bos, L., Bouwman, T., Vaes, W.H.: Predicting blood: air partition coefficients using basic physicochemical properties. Regul. Toxicol. Pharmacol. 62(1), 23–28 (2012)
Sager, J.E., Yu, J., Raguenau-Majlessi, I., Isoherranen, N.: Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications and model verification. Drug Metab. Dispos. 43, 1823–1837 (2015)
Vork, K., Carlisle, J., Brown, J.P.: Estimating Workplace Air and Worker Blood Lead Concentration using an Updated Physiologically-based Pharmacokinetic (PBPK) Model: Office of Environmental Health Hazard Assessment. California Environmental Protection Agency (2013)
Dashtbozorgi, Z., Golmohammadi, H.: Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches. Eur. J. Med. Chem. 45(6), 2182–2190 (2010)
Abraham, M.H., Ibrahim, A., Acree, W.E.: Air to liver partition coefficients for volatile organic compounds and blood to liver partition coefficients for volatile organic compounds and drugs. Eur. J. Med. Chem. 42(6), 743–751 (2007)
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 17(12), 14937–14953 (2012)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. In: ACM SIGKDD Explorations Newsletter, 11(1), pp. 10–18 (2009)
DRAGON 5.5 for Windows (Software for Molecular Descriptor Calculations), Version 5.5. Talete srl, Milan, Italy (2007)
Soto, A.J., Cecchini, R.L., Vazquez, G.E., Ponzoni, I.: Multi-objective feature selection in QSAR using a machine learning approach. QSAR Comb. Sci. 28(11–12), 1509–1523 (2009)
Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J.: Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods. J. Cheminform. 7(1), 1 (2015)
HyperChem (TM), Molecular Modeling System, Release 8.0.7 for Windows. Hypercube, Inc., Gainesville, FL, USA (2009)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)
Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med. 4(2), 627 (2013)
Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)
Acknowledgments
This work is kindly supported by CONICET, grant PIP 112-2012- 0100471 and UNS, grant PGI 24/N042.
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Cravero, F., Martínez, M.J., Díaz, M.F., Ponzoni, I. (2017). QSAR Classification Models for Predicting Affinity to Blood or Liver of Volatile Organic Compounds in e-Health. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_38
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DOI: https://doi.org/10.1007/978-3-319-56154-7_38
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