Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method.

ABSTRACT:

Aim

To construct a reliable computational model for the classification of agonists and antagonists of 5-HT(1A) receptor.

Methods

Support vector machine (SVM), a well-known machine learning method, was employed to build a prediction model, and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters, C and r of the SVM model. The overall dataset used in this study comprised 284 ligands of the 5-HT(1A) receptor with diverse structures reported in the literatures.

Results

A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT(1A) receptor. The predictive accuracy for training and test sets was 0.942 and 0.865, respectively. For comp

SUBMITTER: Zhu XL 

PROVIDER: S-EPMC4002729 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature