The structure–antituberculosis activity relationships study in a series of 5-aryl-2-thio-1,3,4-oxadiazole derivatives (original) (raw)
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Medicinal Chemistry, 2006
Antituberculosis activity of several 5-(4-aminophenyl)-4-alkyl/aryl-2,4-dihydro-3H-1,2,4-triazole-3-thiones (1-9) and their thiourea derivatives (10-31) were screened for their antimycobacterial activities against Mycobacterium tuberculosis H37Rv using the BACTEC 460 radiometric system. Of the synthesized compounds, 10-12, 30 were 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 of prognosis was developed as the result of training associative neural network (ASNN) using weights of pharmacophoric fragments as descriptors. Descriptors were calculated by the projection of ETM compound and pharmacophoric fragments on the elements of Kohonen's self-organizing maps (SOM). From the detailed analysis of all compounds under study, the necessary requirements for a compound to possess antituberculosis activity were formulated. The analysis have shown that any requirements violation for a molecule implies a considerable decrease or even complete loss of its activity.
Journal of Medicinal Chemistry, 2004
A series of 2,5-disubstituted-1,3,4-thiadiazoles were synthesized, the compounds structures were elucidated and screened for the antituberculosis activity against Mycobacterium tuberculosis H37Rv using the BACTEC 460 radiometric system. Among the tested compounds, 2-phenylamino-5-(4-fluorophenyl)-1,3,4-thiadiazole 22 showed the highest inhibitory activity. The relationships between the structures of compounds and their antituberculosis activity were investigated by the Electronic-Topological Method (ETM) and feed forward neural networks (FFNNs) trained with the back-propagation algorithm. As a result of the approach, a system of pharmacophores and anti-pharmacophores has been found that effectively separates compounds of the examination set into groups of active and inactive compounds. The system can be applied to the screening and design of new active compounds possessing skeletons similar to those used in the present study.
Synthesis and structure–antituberculosis activity relationship of 1H-indole-2,3-dione derivatives
Bioorganic & Medicinal Chemistry, 2007
New series of 5-fluoro-1H-indole-2,3-dione-3-thiosemicarbazones 2a-k and 5-fluoro-1-morpholino/piperidinomethyl-1Hindole-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-2,3dione-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-1Hindole-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.
Design of novel antituberculosis compounds using graph-theoretical and substructural approaches
Molecular Diversity, 2009
The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other Electronic supplementary material The online version of this article (pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.
Journal of Bionanoscience, 2018
Qualitative and Quantitative structure-activity relationship (QSAR) studies have been performed on twenty-three molecules of 1,3,4-Thiadiazole derivatives. The compounds used are among the most tubulin inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and tubulin inhibition of the 1,3,4-Thiadiazole derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: polarizability (POL), molar volume (MV), molar weight (MW), partition coefficient octanol/water (logP) and molar refractivity (MR), for the tubulin inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activities values was observed, indicating the validation and the good quality of the QSAR model.
Prediction of antimicrobial activity of imidazole derivatives by artificial neural networks
Central European Journal of Medicine, 2012
The main goal of our study is the analysis of data obtained from molecular modeling for a series of imidazole derivatives that possess strong antifungal activity. The research was designed to use artificial neural network (ANN) analysis to determine quantitative relationships between the structural parameters and anti-Streptococcus pyogenes activity of a series of imidazole derivatives. ANN in association with quantitative structure-activity relationships (QSAR) represents a promising tool in the search for drug candidates among the practically unlimited number of possible derivatives. In this work, a series of 286 imidazole derivatives presented as cationic three-dimensional structures was used. The activity was expressed as a logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Multilayer perceptron ANN was used for predictions of antimicrobial potency of new imidazole derivatives on the basis of their structural descriptors. The obtained correlation coefficient equaled 0.9461 for the learning set, 0.9060 for the validation set and 0.8824 for the testing set of imidazole derivatives. Hence, satisfactory and practically useful predictions of anti-Streptococcus pyogenes activity for a series of imidazole derivatives was obtained, supporting the future successful interpretation of QSAR analysis for those compounds.
2020
A series of novel Schiff bases were designed and synthesized by the condensation of 1,3,4-thiadiazoles that contain aromatic primary amine and variously substituted benzaldehydes. The synthesized compounds were screened for their antituberculosis activity against Mycobacterium tuberculosis H37Rv using BACTEC 460 radiometric system. Among the tested compounds, 2-(4-nitrophenyl)amino-5-[4-(3-(4-phenoxy))benzylideneaminophenyl]-1,3,4thiadiazole (3n) showed the highest inhibitory activity (80%). The activities of the newly synthesized Schiff bases were higher in comparison to those of intermediate products 2-(4-aminophenyl)-5-aryl/alkylamino-1,3,4thiadiazoles (2a-l). The computational studies were also performed to estimate drug-like profile of the compounds by using QikProp analysis.
European Journal of Medicinal Chemistry, 2013
The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R 2 of 0.874 and RMSE of 0.437 against R 2 of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.
Journal of Medicinal Chemistry, 2004
Tuberculosis (TB) kills more youth and adults than any other infectious disease in the world today. The emergence of new strains of Mycobacterium tuberculosis resistant to some or all current antituberculosis drugs is a serious and crescent problem. The resistance is often a corollary to HIV infection and drug-resistant TB is more difficult and more expensive to treat, besides to be more likely fatal. Thus, it is still necessary to search for new antimycobacterial agents. The identification of novel targets need the identification of biochemical pathways specific to mycobacteria and related organisms. Many unique metabolic processes occur during the biosynthesis of mycobacterial cell wall components. In this report, we examine one of these attractive targets for the rational design of new antituberculosis agents -the mycolic acids.
K Nearest Neighbor and 3D QSAR Analysis of Thiazolidinone Derivatives as Antitubercular Agents
Journal of Pharmaceutical Research, 2016
The present research communication describes development of kNN and 3D QSAR models for identification of structural features which are responsible for antimycobacterial activity of Thiazolidinone. In the present work, two predictiveof kNN and 3D QSAR models were developed via utilization of multiple linear regression analysis. MLR analysis was carried out on reported dataset of thiazolidinone as Antimycobacterial. Vlife MDS 4.4 is utilized for development of kNN and 3D QSAR models which were validated via internal test set. Two different kNN and 3D QSAR models developed for dataset of thiazolidinone molecules as antimycobacterial. The Model A and Model B describes the best selected 3D QSAR model predicting antimycobacterial activity of the thiazolidinone derivatives. 3D QSAR model A is best selected model which indicates steric interaction fields needs to be minimized while electrostatic interaction field needs to be improved for potential increase in antimycobacterial activity. The Model C and D are two selected kNN models for anti-mycobacterial activity of the thiazolidinone derivatives. Model D is better fitted kNN model describing negative contribution of the electrostatic interaction fields and positive contribution of the steric interaction field. The review of literature revealed QSAR analysis plays vital role in the development of the novel drug like candidates. Thiazolidinone derivatives were reported for their antimycobacterial potential but their quantitative measures were not reported. These facts prompted us to for development of QSAR models which will be utilized for development of potent and selective antimycobacterial agents. The study revealed that 3DQSAR model A and kNN model D better describes the antimycobacterial potential of the thiazolidinone derivatives. Substitution of the smaller groups on the aromatic ring bearing thiazolidinone nucleus will increase the antimycobacterial potential of the thiazolidinone derivatives.