The regression QSAR models were built to predict the antimicrobial activity of new thiazole derivatives. Compounds with high predicting activity were synthesized and evaluated against Gram-positive and Gram-negative bacteria and fungi. 1,3-Thiazole-4-ylphosphonium salts 4 and 5 displayed good antibacterial properties and high antifungal activity. The predictions are in a good agreement with the experiment results, which indicate the good predictive power of the created QSAR models.

QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR) clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2=0.72-0.8. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2=0.74-0.8). The models were applied to screen a virtual chemical library of cytisine derivatives, which was designed to have antibacterial activity. The QSAR modeling results allowed to identify a number of cytisine derivatives as effective antibacterial agents against antibiotic-resistant E. coli strains. Seven compounds were selected for synthesis and biological testing. In vitro investigation of the selected cytisine derivatives have shown that all studied compounds are potential antibacterial agents against MDR E. coli strains