Particle swarm optimization and genetic algorithm as feature selection techniques for the QSAR modeling of imidazo[1,5-a]pyrido[3,2-e]pyrazines, inhibitors of phosphodiesterase 10A

Mohammad Goodarzi, Wouter Saeys, Omar Deeb, Sigrid Pieters, Yvan Vander Heyden

Research output: Contribution to journalArticle

14 Scopus citations


Quantitative structure-activity relationship (QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization (PSO) and genetic algorithm (GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression (MLR)] and non-linear [locally weighted regression (LWR) based on both Euclidean (E) and Mahalanobis (M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50. The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction (RMSEP) for the test sets obtained by PSO/MLR, GA/MLR, PSO/LWRE, PSO/LWRM, GA/LWRE, and GA/LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear. Molecular Descriptors are calculated based on the structure determination. GA and PSO are applied as Feature selection methods. QSAR models are built with linear and nonlinear techniques (only when few descriptors are selected).

Original languageEnglish (US)
Pages (from-to)685-696
Number of pages12
JournalChemical Biology and Drug Design
Issue number6
StatePublished - Dec 1 2013



  • Euclidean distance
  • Genetic algorithm
  • Locally weighted regression
  • Mahalanobis distance
  • Multiple linear regression
  • Particle swarm optimization

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine

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