NDLI: QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties

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QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties

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Journal of Molecular Modeling : Volume 13, Issue 3, March 2007

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QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties

Content Provider	Springer Nature Link
Author	Fernández, Michael Caballero, Julio
Copyright Year	2007
Abstract	Multiple linear regression (MLR) combined with genetic algorithm (GA) and Bayesian-regularized Genetic Neural Networks (BRGNNs) were used to model the binding affinity (pKI) of 38 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A for the Human Luteinizing Hormone-Releasing Hormone (LHRH) receptor using quantum chemical descriptors. A multiparametric MLR equation with good statistical quality was obtained that describes the features relevant for antagonistic activity when the substituent at the position 3 of the erythronolide core was varied. In addition, four-descriptor linear and nonlinear models were established for the whole dataset. Such models showed high statistical quality. However, the BRGNN model was better than the linear model according to the external validation process. In general, our linear and nonlinear models reveal that the binding affinity of the compounds studied for the LHRH receptor is modulated by electron-related terms. Figure 1 General structure and numbering of the 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A used in this study
Starting Page	465
Ending Page	476
Page Count	12
File Format	PDF
ISSN	16102940
Journal	Journal of Molecular Modeling
Volume Number	13
Issue Number	4
e-ISSN	09485023
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-01-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	QSAR analysis Bayesian-regularized Genetic Neural Network Quantum chemical descriptors Macrolide LHRH antagonists Computer Application in Life Sciences Life Sciences Health Informatics Molecular Medicine Biomedicine general Computer Applications in Chemistry
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Physical and Theoretical Chemistry Computational Theory and Mathematics Catalysis Inorganic Chemistry Computer Science Applications

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