Computer-aided design of novel antibacterial 3-hydroxypyridine-4-ones: application of QSAR methods based on the MOLMAP approach

Abstract

3-Hydroxypyridine-4-one derivatives have shown good inhibitory activity against bacterial strains. In this work we report the application of MOLMAP descriptors based on empirical physicochemical properties with genetic algorithm partial least squares (GA-PLS) and counter propagation artificial neural networks (CP-ANN) methods to propose some novel 3-hydroxypyridine-4-one derivatives with improved antibacterial activity against Staphylococcus aureus. A large collection of 302 novel derivatives of this chemical scaffold was selected for this purpose. The activity classes of these compounds were determined using the two quantitative structure activity relationships models. To evaluate the predictability and accuracy of the obtained models, nineteen compounds belonging to all three activity classes were prepared and the activity of them was determined against S. aureus. Comparing the experimental results and the predicted activity classes revealed the accuracy of the obtained models. Seventeen of the nineteen synthesized molecules were correctly predicted by GA-PLS model according to the antimicrobial evaluation method. Molecules 5f and 5h proved to be moderately active and active experimentally, but were predicted as inactive and moderately active compounds, respectively by this model. The CP-ANN based prediction was correct for sixteen out of the nineteen synthesized molecules. 5a, 5h and 5q were moderately active and active based on the antimicrobial assays, but they were introduced as members of inactive, moderately active and inactive classes of compounds, respectively according to CP-ANN model.