title: MIA-QSAR evaluation of a series of sulfonylurea herbicides.
abstract: BACKGROUND
  A series of sulfonylurea herbicides has been modelled using a 2D image-based QSAR approach known as MIA-QSAR (Multivariate Image Analysis applied to QSAR), and highly predictive models have been built.
  
  
  RESULTS
  Two MIA-QSAR models were built, one group being divided into training and test sets, and the other composed of the entire series of compounds. Statistically significant MIA-QSAR models rendered high correlation coefficients of experimental versus fitted pK(i)(app) (AHAS apparent inhibition constant) and satisfactory parameters of external validation and leave-one-out cross-validation. Comparison with the results obtained from classical 2D QSAR demonstrated some advantages of the modelling using MIA descriptors.
  
  
  CONCLUSIONS
  Both MIA-QSAR models showed high predictive ability, comparable with that of a reference methodology based on 3D descriptors. The method is suggested as a suitable tool for predicting novel herbicides.
authors.affiliation: Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000 Lavras, MG, Brazil.
authors.forename: Michelle
authors.initials: M
authors.lastname: Bitencourt
authors.forename: Matheus P
authors.initials: MP
authors.lastname: Freitas
mesh_terms: D006540:Herbicides / Q000737:chemistry* / Q000494:pharmacology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008958:Models, Molecular; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012986:Software Validation; D013453:Sulfonylurea Compounds / Q000737:chemistry* / Q000494:pharmacology
chemical_list: D006540:Herbicides; D013453:Sulfonylurea Compounds