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Hui Zhang Ming-Li Xiang Chang-Ying Ma Qi Huang Wei Li Yang Xie Yu-Quan Wei Sheng-Yong Yang 《Molecular diversity》2009,13(2):261-268
In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed
by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate
gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to
evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for
test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both
logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class
models. A comparison between the performance of GA–CG–SVM models and that of GA–SVM models shows that the SVM parameter optimization
has a significant impact on the quality of SVM classification model.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.
Hui Zhang and Ming-Li Xiang are contributed equally. 相似文献
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Julio Caballero Ariela Vergara-Jaque Michael Fernández Deysma Coll 《Molecular diversity》2009,13(4):493-500
We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc)
to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set
of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC50) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure–activity relationship (QSAR) method
using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron
diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters
[cross-validate correlation coefficient (Q
2 = 0.796) and fitted correlation coefficient (R
2 = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted
four compounds that were not included in the training set. 相似文献
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QSAR modelling of carcinogenicity by balance of correlations 总被引:1,自引:0,他引:1
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Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of
GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors.
In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models
for GSK−3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different
GSK−3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR
models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds
(93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using
an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637
out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three
types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1
which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different
fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design
new inhibitors of GSK−3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK−3α inhibitors against different molecules found in the literature. 相似文献
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