Title: Support Vector Regression based QSAR of anti-Haemophilus Influenzae activity of orally administered cephalosporins

Authors: Qin Yang, Wen-Cong Lu, Xu Liu, Tian-Hong Gu

Addresses: Department of Chemistry, School of Science, Shanghai University, Shanghai 200444, China. ' Department of Chemistry, School of Science, Shanghai University, Shanghai 200444, China. ' Department of Chemistry, School of Science, Shanghai University, Shanghai 200444, China. ' Department of Chemistry, School of Science, Shanghai University, Shanghai 200444, China

Abstract: Support Vector Regression (SVR), a novel robust machine learning technology, was applied to QSAR on the anti-Haemophilus Influenzae (HI) activity of 69 orally active cephalosporins. The optimal model was built with three descriptors-MR, qC7 and qO9, which came from 23 descriptors available. The prediction accuracy of the model was discussed on the basis of Leave-One-Out Cross-Validation (LOOCV) and the independent test dataset. Eighteen newly designed molecules are highly recommended for synthesis scientists based on the SVR model obtained.

Keywords: SVR; support vector regression; QSAR; cephalosporins; anti-HI; anti-haemophilus influenzae activity; machine learning; prediction accuracy; molecule design; haemophilus influenzae; quantitative structure activity relationship; modelling; synthesis.

DOI: 10.1504/IJFIPM.2009.022840

International Journal of Functional Informatics and Personalised Medicine, 2009 Vol.2 No.1, pp.104 - 123

Available online: 27 Jan 2009 *

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