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 r2> 0.85 and r2> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q2 = 0.83, R2 = 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.
Original language | English |
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Pages (from-to) | 75-82 |
Number of pages | 8 |
Journal | Current Computer-Aided Drug Design |
Volume | 10 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- 4-aminoquinolines
- Anti-malarial activity
- Coral software
- Optimal descriptor
- QSAR
ASJC Scopus subject areas
- Drug Discovery
- Molecular Medicine
- Medicine(all)