QSAR study of active human glucagon receptor antagonists by SW-MLR and SW-SVM methods
2014 (English)In: Medicinal Chemistry Research, ISSN 1054-2523, E-ISSN 1554-8120, Vol. 23, no 5, 2639-2650 p.Article in journal (Refereed) Published
A linear quantitative structure-activity relationship model is given for modelling and predicting of human glucagon receptor activities. A dataset containing 37 human glucagon receptors with their corresponding inhibition activities were used. The whole dataset was divided into training and testing set by using hierarchical clustering technique. Seven variables were selected by the stepwise variable selection procedure. Multiple linear regressions (MLR) and support vector machine (SVM) were applied to model the relationship between biological activities and molecular descriptors. Both models could suggest satisfactory prediction results: MLR method presented squared correlation coefficients of R (2) for the training and test sets of 0.894 and 0.776, namely, and squared correlation coefficient of 0.999 and 0.824 was obtained for training and testing sets by SVM model, respectively. The prediction result of the SVM was superior to that obtained by MLR model. The given models have suitable predictability and stability and can culminate in designing novel and potent human glucagon receptor activities.
Place, publisher, year, edition, pages
Springer, 2014. Vol. 23, no 5, 2639-2650 p.
QSAR, multiple linear regression, stepwise, support vector machine, human glucagon receptor
Pharmacology and Toxicology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
IdentifiersURN: urn:nbn:se:umu:diva-87623DOI: 10.1007/s00044-013-0851-6ISI: 000332153800045OAI: oai:DiVA.org:umu-87623DiVA: diva2:711695