title: QSAR models for anti-malarial activity of 4-aminoquinolines.
abstract: In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.
authors.forename: Vijay H
authors.initials: VH
authors.lastname: Masand
authors.forename: Andrey A
authors.initials: AA
authors.lastname: Toropov
authors.forename: Alla P
authors.initials: AP
authors.lastname: Toropova
authors.affiliation: (Vijay H. Masand) Department of Chemistry, Vidya Bharati College, Camp, Amravati, Maharashtra, 444 602, India. vijaymasand@gmail.com.
authors.forename: Devidas T
authors.initials: DT
authors.lastname: Mahajan
mesh_terms: D000465:Algorithms; D000634:Aminoquinolines / Q000737:chemistry* / Q000494:pharmacology*; D000962:Antimalarials / Q000737:chemistry* / Q000494:pharmacology*; D001185:Artificial Intelligence; D015195:Drug Design; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D000634:Aminoquinolines; D000962:Antimalarials