Quantitative Structure-Activity Relationship Study on Thiosemicarbazone Derivatives as Antitubercular agents Using Artificial Neural Network and Multiple Linear Regression

Authors

  • Daryaee, Fereidoon Assistant Professor, Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
  • Hassanzadeh, Abdolreza Assistant Professor, Pharmaceutics Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
  • Mohseni, Behnam PhD in Chemistry, Department of Chemistry, Shahid Bahonar University of Kerman, Kerman, Iran
  • Mousavi, Mehdi 1Associate Professor, Department of Chemistry, Shahid Bahonar University of Kerman, Kerman, Iran
  • Ranjbaran, Omid Doctor of Pharmacy, Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
  • Taheri, Saeideh Doctor of Pharmacy, Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
Abstract:

Background and purpose: Nonlinear analysis methods for quantitative structure–activity relationship (QSAR) studies better describe molecular behaviors, than linear analysis. Artificial neural networks are mathematical models and algorithms which imitate the information process and learning of human brain. Some S-alkyl derivatives of thiosemicarbazone are shown to be beneficial in prevention and treatment of mycobacterial infections and this study seeks to find out the relationship between structural features and the anti-tuberculosis activity of these compounds. Materials and methods: Multiple linear regression and Bayesian regularized artificial neural network (BRANN) for 47 compounds of thiosemicarbazone derivatives were designed using QSAR approaches. Descriptors were selected from a pool of 343 descriptors by stepwise selection and backward elimination. A three layer Bayesian regularized back-propagation feed-forward network was designed, optimized, and evaluated using MATLAB version R2009a. Results: The best model with 6 descriptors was found using multiple linear regression analysis: Log MIC= 2.592 + (0.067 ± 0.018) PMIX – (0.066 ± 0.017) PMIZ – (1.706 ± 1.600) Qneg – (0.235 ± 0.039) RDF030p + (0.118 ± 0.026) RDF 140u – (0.064 ± 0.021) RDF060p. The best BRANN model was a three-layer network with three nodes in its hidden layer. Conclusion: The BRANN model has a better predictive power than linear models and may better predict the anti-tuberculosis activity of new compounds with similar backbone of thiosemicarbazone moiety.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

EVALUATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION MODELS

In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...

full text

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Artificial Neural Network and Non-Linear Regression: A Comparative Study

Indian summer monsoon rainfall (ISMR) is an important metric to quantify the Asian monsoon system. Artificial Neural Networks, ANNs, are being increasingly used for nonlinear regression and classification problems in meteorology. The issues raised for this study can be summarized as the problem of simulation of the ISMR time series with the ANN model to get away with the need of external parame...

full text

prediction of amino acids contents in corn and wheat by using artificial neural network model and multiple linear regression

to determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. in the current laboratories, digestion nirs method is widely used for this purpose. but this method has technical limitation. therefor is important find appropriate method for estimate amount of amino acids. artificial neural network (ann) can provide a bet...

full text

Synthesis of Some 4-Thiazolidinone Derivatives as Antitubercular Agents

Substituted Schiff's bases 2a-o prepared by the treatment of 2-amino-4-(?-methoxyiminocarbomethoxymethyl)-thiazole 1 with different aromatic aldehydes, on cyclocondensation with mercaptoaceticacid and mercaptopropionicacid in dry benzene furnished desired thiazolidinones of type 3a-o and 4a-j, respectively. The structure of the compounds have been assigned on the basis of elemental analyses and...

full text

Synthesis and structure−activity relationship of 8-substituted protoberberine derivatives as a novel class of antitubercular agents

BACKGROUND The emergence of multi-drug resistant tuberculosis (MDR-TB) has heightened the need for new chemical classes and innovative strategies to tackle TB infections. It is urgent to discover new classes of molecules without cross-resistance with currently used antimycobacterial drugs. RESULTS Eighteen new 8-substituted protoberberine derivatives were synthesized and evaluated for their a...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 30  issue 184

pages  106- 118

publication date 2020-05

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

No Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023