title: Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method.
abstract: OBJECTIVE
  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 compounds with probability estimate higher than 0.7, the predictive accuracy of the model for training and test sets was 0.954 and 0.927, respectively. To further validate our model, the receiver operating characteristic (ROC) curve was plotted, and the Area-Under-the-ROC- Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.
  
  
  CONCLUSIONS
  A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT(1A) receptor. To our knowledge, this is the first effort for the classification of 5-HT(1A) receptor agonists and antagonists based on a diverse dataset. This method may be used to classify the ligands of other members of the GPCR family.
authors.affiliation: Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Xue-lian
authors.initials: XL
authors.lastname: Zhu
authors.forename: Hai-yan
authors.initials: HY
authors.lastname: Cai
authors.forename: Zhi-jian
authors.initials: ZJ
authors.lastname: Xu
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: He-yao
authors.initials: HY
authors.lastname: Wang
authors.forename: Ao
authors.initials: A
authors.lastname: Zhang
authors.forename: Wei-liang
authors.initials: WL
authors.lastname: Zhu
mesh_terms: D001185:Artificial Intelligence; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D044282:Receptor, Serotonin, 5-HT1A / Q000378:metabolism*; D058825:Serotonin 5-HT1 Receptor Agonists / Q000494:pharmacology*; D058829:Serotonin 5-HT1 Receptor Antagonists / Q000494:pharmacology*
chemical_list: D058825:Serotonin 5-HT1 Receptor Agonists; D058829:Serotonin 5-HT1 Receptor Antagonists; D044282:Receptor, Serotonin, 5-HT1A