Current Computer-Aided Drug Design

Subhash C. Basak
Departments of Chemistry, Biochemistry & Molecular Biology University of Minnesota Duluth
Duluth, MN 55811


Prediction of Thrombin and Factor Xa Inhibitory Activity with Associative Neural Networks

Author(s): Vasyl Kovalishyn, Vsevolod Tanchuk, Iryna Kopernyk, Volodymyr Prokopenko, Larysa Metelytsia.


Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74 - 0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71 - 0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.

Keywords: Drug design, factor Xa, QSAR, Neural Networks, thrombin.

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Article Details

Year: 2014
Page: [259 - 265]
Pages: 7
DOI: 10.2174/157340991003150302231419
Price: $58