New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists
Journal of molecular modeling(2017)
摘要
The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r 2 training = 0.761, q 2 = 0.656, r 2 test = 0.746, MSE test = 0.132 and MAE test = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSE test values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r 2 test = 0.824, MSE test = 0.088 and MAE test = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r 2 test = 0.811, MSE test = 0.100 and MAE test = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.
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关键词
Sigma-1 receptor,1-arylpyrazole,QSAR,PLS,MLP-ANN,Consensus modeling
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