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.
Current Computer-Aided Drug Design
Title:Prediction of Thrombin and Factor Xa Inhibitory Activity with Associative Neural Networks
Volume: 10 Issue: 3
Author(s): Vasyl Kovalishyn, Vsevolod Tanchuk, Iryna Kopernyk, Volodymyr Prokopenko and Larysa Metelytsia
Affiliation:
Keywords: Drug design, factor Xa, QSAR, Neural Networks, thrombin.
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.
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Cite this article as:
Kovalishyn Vasyl, Tanchuk Vsevolod, Kopernyk Iryna, Prokopenko Volodymyr and Metelytsia Larysa, Prediction of Thrombin and Factor Xa Inhibitory Activity with Associative Neural Networks, Current Computer-Aided Drug Design 2014; 10 (3) . https://dx.doi.org/10.2174/157340991003150302231419
DOI https://dx.doi.org/10.2174/157340991003150302231419 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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