title: Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.
abstract: BACKGROUND
  Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates.
  
  
  METHODS
  First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models.
  
  
  RESULTS
  A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models.
  
  
  CONCLUSIONS
  The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.
authors.forename: Gene M
authors.initials: GM
authors.lastname: Ko
authors.affiliation: Computational Science Research Center, San Diego State University, San Diego, CA, Address: 5500 Campanile Drive, San Diego, CA 92182-1245, USA. rg004747@yahoo.com.
authors.forename: Rajni
authors.initials: R
authors.lastname: Garg
authors.forename: Barbara A
authors.initials: BA
authors.lastname: Bailey
authors.forename: Sunil
authors.initials: S
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D019428:HIV Integrase Inhibitors / Q000032:analysis* / Q000737:chemistry* / Q000494:pharmacology; D008958:Models, Molecular; D015394:Molecular Structure; D054547:National Cancer Institute (U.S.); D021281:Quantitative Structure-Activity Relationship; D014481:United States
chemical_list: D019428:HIV Integrase Inhibitors