title: Prediction of thrombin and factor xa inhibitory activity with associative neural networks.
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.
authors.affiliation: Institute of Bioorganic Chemistry & Petroleum Chemistry, National Ukrainian Academy of Sciences, Kyiv-94, 02660, Murmanska, 1, Ukraine. vkovalishyn@yahoo.com.
authors.forename: Vasyl
authors.initials: V
authors.lastname: Kovalishyn
authors.forename: Vsevolod
authors.initials: V
authors.lastname: Tanchuk
authors.forename: Iryna
authors.initials: I
authors.lastname: Kopernyk
authors.forename: Volodymyr
authors.initials: V
authors.lastname: Prokopenko
authors.forename: Larysa
authors.initials: L
authors.lastname: Metelytsia
mesh_terms: D000991:Antithrombins / Q000737:chemistry / Q000494:pharmacology*; D015195:Drug Design; D015951:Factor Xa / Q000187:drug effects; D065427:Factor Xa Inhibitors / Q000494:pharmacology*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D013917:Thrombin / Q000187:drug effects
chemical_list: D000991:Antithrombins; D065427:Factor Xa Inhibitors; D013917:Thrombin; D015951:Factor Xa