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A series of 82 5-aryl-2-thio-1,3,4-oxadiazole derivatives were screened for their anti-mycobacterial activities against Mycobacterium tuberculosis H37Rv. The synthesized compounds 30–37 appeared to be the most active derivatives exhibiting more than 90% inhibition of mycobacterial growth at 12.5 μg/mL. Structure–activity relationships study was performed for the given series by using the electronic-topological method combined with neural networks (ETM–NN). A system for the anti-mycobacterial activity prediction was developed as the result of training associative neural network (ASNN) with weights calculated from projections of a compound and each pharmacophoric fragment found on the elements of the Kohonen’s self-organizing maps (SOMs). From the detailed analysis of all compounds under study, the necessary requirements for a compound to possess antituberculosis activity have been formulated. The analysis has shown that any requirement’s violation for a molecule implies a considerable decrease or even complete loss of its activity. Molecular docking studies of the compounds allowed shedding light on the binding mode of these novel anti-mycobacterial inhibitors.

New series of 5-fluoro-1H-indole-2,3-dione-3-thiosemicarbazones 2a-k and 5-fluoro-1-morpholino/piperidinomethyl-1H-indole-2,3-dione-3-thiosemicarbazones 3a-r were synthesized. The structures of the synthesized compounds were confirmed by spectral data, elemental and single crystal X-ray diffraction analysis. The new 5-fluoro-1H-indole-2,3-dione derivatives, along with previously reported 5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones 2l-v, 1-morpholino/piperidinomethyl-5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones 4a-l, and 5-nitro-1H-indole-2,3-dione-3-[(4-oxo-1,3-thiazolidin-2-ylidene)hydrazones] 5a-s, were evaluated for in vitro antituberculosis activity against Mycobacterium tuberculosis H37Rv. Among the tested compounds, 5-nitro-1H-indole-2,3-dione-3-thiosemicarbazones (2p, 2r, and 2s) and its 1-morpholinomethyl derivatives (4a, 4e, 4g, and 4i) exhibited significant inhibitory activity in the primary screen. The antituberculosis activity of molecules with diverse skeletons was investigated by means of the Electronic-Topological Method (ETM). Ten pharmacophores and ten anti-pharmacophores that have been found by this form the basis of the system capable of predicting the structures of potentially active compounds. The forecasting ability of the system has been tested on structures that differ from those synthesized. The probability of correct identification for active compounds was found as equal to 93% in average. To obtain the algorithmic base for the activity prediction, Artificial Neural Networks were used after the ETM (the so-called combined ETM-ANN method). As the result, only 9 pharmacophores and anti-pharmacophores were chosen as the most important ones for the activity. By this, ANNs classified correctly 94.4%, or 67 compounds from 71.

The preparation of novel 5-aryl-2-thio-1,3,4-oxadiazoles 4a-41 and the computer-aided study of their in vitro anti-tubercular activity against Mycobacterium tuberculosis H37Rv (ATCC 27294) are reported. The average accuracy of the electronic-topological method and neural network methods applied to the activity prediction in leave-one-out cross validation is 80%.

The synthesis of 2,3,5-substituted perhydropyrrolo[3,4-d]isoxazole-4,6-diones (44 compounds) has been accomplished by the cycloaddition reaction of N-methyl-C-arylnitrones with N-substituted maleimides. The compounds were screened for their antibacterial activities and most of them exhibited activity against Enterococcus faecalis (ATCC 29212) and Staphylococcus aureus (ATCC 25923). cis-3a and cis-3d were found fairly effective against E. faecalis (ATCC 29212) and S. aureus (ATCC 25923) with MIC values of 25 and 50microg/ml. With the changes of cis isomers of the compounds to trans, their antibacterial activities also changed against the bacteria studied. First, pharmacophoric fragments had been calculated in accordance with the rules of the electronic-topological method (ETM). Next, both active compounds and pharmacophores had been projected to the nodes of Kohonen's self-organizing maps (SOM) to obtain the weights of pharmacophore fragments as numerical descriptors, that were used after this for the associative neural networks (ASNN) training. A model for the activity prediction was developed as the result of training the ASNNs.