High-dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues based on support vector regression (SVR)

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dc.contributor.author Zhou, Wei
dc.contributor.author Zhijun, Dai
dc.contributor.author Chen, Yuan
dc.contributor.author Wang, Haiyan
dc.contributor.author Yuan, Zheming
dc.date.accessioned 2012-05-11T17:47:10Z
dc.date.available 2012-05-11T17:47:10Z
dc.date.issued 2012-05-11
dc.identifier.uri http://hdl.handle.net/2097/13813
dc.description.abstract To design ARC-111 analogues with improved efficiency, we constructed the QSAR of 22 ARC-111 analogues with RPMI8402 tumor cells. First, the optimized support vector regression (SVR) model based on the literature descriptors and the worst descriptor elimination multi-roundly (WDEM) method had similar generalization as the artificial neural network (ANN) model for the test set. Secondly, seven and 11 more effective descriptors out of 2,923 features were selected by the high-dimensional descriptor selection nonlinearly (HDSN) and WDEM method, and the SVR models (SVR3 and SVR4) with these selected descriptors resulted in better evaluation measures and a more precise predictive power for the test set. The interpretability system of better SVR models was further established. Our analysis offers some useful parameters for designing ARC-111 analogues with enhanced antitumor activity. en_US
dc.relation.uri http://www.mdpi.com/1422-0067/13/1 en_US
dc.subject ARC-111 analogues en_US
dc.subject QSAR en_US
dc.subject Support vector regression en_US
dc.subject High-dimensional descriptor selection nonlinearly (HDSN) method en_US
dc.subject Worst descriptor elimination multi-roundly (WDEM) method en_US
dc.subject RPMI8402 en_US
dc.title High-dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues based on support vector regression (SVR) en_US
dc.type Article (publisher version) en_US
dc.date.published 2012 en_US
dc.citation.doi doi:10.3390/ijms13011161 en_US
dc.citation.epage 1172 en_US
dc.citation.issue 1 en_US
dc.citation.jtitle International Journal of Molecular Sciences en_US
dc.citation.spage 1161 en_US
dc.citation.volume 13 en_US
dc.contributor.authoreid hwang en_US


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