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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.

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Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously.

A series of 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives were subjected to quantitative structure-antimicrobial activity relationships (QSAR) analysis. A collection of chemometrics methods, including factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR) and partial least squares combined with genetic algorithm for variable selection (GA-PLS) were employed to make connections between structural parameters and antimicrobial activity. The results revealed the significant role of topological parameters in the antimicrobial activity of the studied compounds against S. aureus and C. albicans. The most significant QSAR model, obtained by GA-PLS, could explain and predict 96% and 91% of variances in the pIC 50 data (compounds tested against S. aureus) and predict 91% and 87% of variances in the pIC 50 data (compounds tested against C. albicans), respectively.

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