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