Kovalishyn Vasyl, Tanchuk Vsevolod, Kopernyk Iryna, Prokopenko Volodymyr, Metelytsia Larysa
Institute of Bioorganic Chemistry & Petroleum Chemistry, National Ukrainian Academy of Sciences, Kyiv-94, 02660, Murmanska, 1, Ukraine.
Curr Comput Aided Drug Des. 2014;10(3):259-65. doi: 10.2174/157340991003150302231419.
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.
利用联想神经网络对一系列凝血酶和Xa因子选择性抑制剂进行了定量构效关系研究。为克服因描述符选择导致的过拟合问题，在分析的每个步骤中进行了带有变量选择的5折交叉验证。通过留一法交叉验证对模型的预测能力进行了测试，回归模型的Q2值为0.74 - 0.87。对外部评估集的预测回归准确率在0.71 - 0.82范围内。所提出的模型可能成为寻找新药候选物的潜在工具。
