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Carbonic anhydrase is a metalloprotein, an enzyme with strong inhibition in antibacterial treatment. This study presents QSAR modeling for a series of 41 chemical compounds, 40 sulfonamides and one sulfamate, including 13 clinically tested drugs as carbonic anhydrase inhibitors based on the Monte Carlo optimization with molecular descriptors based on the SMILES notation and local invariants of the molecular graph, and field 3D based methods. Conformation independent QSAR models were developed for three random splits and a 3D QSAR model for one random split into the training and test sets. The statistical quality of the developed models, including robustness and predictability, was tested using various statistical approaches and the results that were obtained were very good. An excellent correlation between the results from the conformation independent and the 3D QSAR model was obtained. A novel statistical metric known as the index of ideality of correlation was used for the final assessment of the model, and the obtained results were good. Molecular fragments responsible for the increases and decreases of a studied activity were defined and further used for the computer-aided design of new compounds as potential carbonic anhydrase inhibitors. Molecular docking was applied for the final assessment of the developed QSAR model and designed inhibitors, and an excellent correlation between the results from QSAR modeling and molecular docking studies was obtained.Communicated by Ramaswamy H. Sarma.

Tuberculosis (TB) is an ancient infectious disease, which re-emerged with the appearance of multidrug-resistant strains and acquired immune deficiency syndrome. Enoyl-acyl-carrier protein reductase (InhA) has emerged as a promising target for the development of anti-tuberculosis therapeutics. This study aims to develop quantitative structure-activity relationship (QSAR) models for a series of arylcarboxamides as InhA inhibitors. The QSAR models were calculated on the basis of optimal molecular descriptors based on the simplified molecular-input line-entry system (SMILES) notation with the Monte Carlo method as a model developer. The molecular docking study was used for the final assessment of the developed QSAR model and designed novel inhibitors. Methods used for the validation indicated that the predictability of the developed model was good. Structural indicators defined as molecular fragments responsible for increases and decreases of the studied activity were defined. The computer-aided design of new compounds as potential InhA inhibitors was presented. The Monte Carlo optimization was capable of being an efficient in silico tool for developing a model of good statistical quality. The predictive potential of the applied approach was tested and the robustness of the model was proven using different methods. The results obtained from molecular docking studies were in excellent correlation with the results from QSAR studies. This study can be useful in the search for novel anti-tuberculosis therapeutics based on InhA inhibition. Communicated by Ramaswamy H. Sarma.