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

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

Journal Name: Current Computer-Aided Drug Design

Volume 10 , Issue 3 , 2014


Become EABM
Become Reviewer
Call for Editor

Abstract:

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.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 10
ISSUE: 3
Year: 2014
Page: [259 - 265]
Pages: 7
DOI: 10.2174/157340991003150302231419
Price: $65

Article Metrics

PDF: 29