Strip mining for molecules

Quantitative structure-activity relationship (QSAR) problems deal with "in-silico" chemical design for the virtual invention of novel pharmaceuticals. The goal of QSAR is to predict the bioactivities of molecules based on a set of descriptive features. QSAR problems are notoriously challen...

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Bibliographic Details
Published inProceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Vol. 1; pp. 305 - 310 vol.1
Main Authors Embrechts, M.J., Arciniegas, F., Ozdemir, M., Momma, M., Breneman, C.M., Lockwood, L., Bennett, K.P., Kewley, R.H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Chemicals
Data mining
Design engineering
Drugs
Human immunodeficiency virus
Pharmaceuticals
Predictive models
Principal component analysis
Sensitivity analysis
Strips
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Summary:Quantitative structure-activity relationship (QSAR) problems deal with "in-silico" chemical design for the virtual invention of novel pharmaceuticals. The goal of QSAR is to predict the bioactivities of molecules based on a set of descriptive features. QSAR problems are notoriously challenging for machine learning because a typical QSAR predictive data mining problem set is characterized by a large number of descriptive features (300-1000), often for a relatively small number of molecules (50-300). This paper introduces data strip mining for QSAR modeling. Strip mining is a general approach for feature selection and predictive modeling based on successive stages of feature elimination done by performing a sensitivity analysis to a predictive model.
ISBN:9780780372788
0780372786
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.2002.1005488