QSAR study on the histamine (H3) receptor antagonists using the genetic algorithm: Multi parameter linear regression

• Main Authors: Adimi Maryam, Salimi Mahmoud, Nekoei Mehdi
• Format: Article
• Language: English
• Published: Serbian Chemical Society 2012-01-01
• Series: Journal of the Serbian Chemical Society
• Subjects: QSAR; genetic algorithm; multiple linear regression; biphenyl derivatives; histamine (H3) receptors
• Online Access: http://www.doiserbia.nb.rs/img/doi/0352-5139/2012/0352-51391100205A.pdf

A quantitative structure activity relationship (QSAR) model has been produced for predicting antagonist potency of biphenyl derivatives as human histamine (H3) receptors. The molecular structures of the compounds are numerically represented by various kinds of molecular descriptors. The whole data set was divided into training and test sets. Genetic algorithm based multiple linear regression is used to select most statistically effective descriptors. The final QSAR model (N =24, R2=0.916, F = 51.771, Q2 LOO = 0.872, Q2 LGO = 0.847, Q2 BOOT = 0.857) was fully validated employing leaveone- out (LOO) cross-validation approach, Fischer statistics (F), Yrandomisation test, and predictions based on the test data set. The test set presented an external prediction power of R2 test=0.855. In conclusion, the QSAR model generated can be used as a valuable tool for designing similar groups of new antagonists of histamine (H3) receptors.