Статья

Multiple Linear Regression Analysis and Optimal Descriptors: Predicting the Cholesteryl Ester Transfer Protein Inhibition Activity

Bakhtiyor RasulevDepartment of Chemistry, Computational Center for Molecular Structure and Interactions, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS 39217, USAAndrey A. ToropovDepartment of Chemistry, Computational Center for Molecular Structure and Interactions, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS 39217, USAAshton T. HammeDepartment of Chemistry, Computational Center for Molecular Structure and Interactions, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS 39217, USAJerzy LeszczyńskiDepartment of Chemistry, Computational Center for Molecular Structure and Interactions, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS 39217, USA

2007en

ABI

Аннотация

Abstract Quantitative structure–activity relationships have been developed for a set of 40 halogenated substituted N ‐benzyl‐ N ‐phenyl aminoalcohol compounds. IC 50 values for Cholesteryl Ester Transfer Protein (CETP) inhibition activity for these compounds expressed in log units have been modeled by Multiple Linear Regression Analysis (MLRA) based on descriptors generated by DRAGON software and optimal descriptors approach. Forty benzene derivatives have been divided into training ( n =20) and test ( n =20) sets. In the case of the MLRA, the three‐variable model has the best predictive potential. Comparison of the quality of MLRA and optimal descriptor models shows that the predictive potential of the one‐variable model, based on the optimal descriptor, provides a low correlation coefficient in the training set. However, this one‐variable model performs better for the test set when compared to the two‐ and three‐variable MLRA models.

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Идентификаторы

DOI: 10.1002/qsar.200710006

Цитирования и источники

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