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

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MOUSAVI MEHDI; Daryaee Fereidoon; Ranjbaran Omid; MOHSENI BEHNAM; Taheri Saeideh; Hassanzadeh Abdolreza

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Journal: Journal of Mazandaran University of Medical Sciences Year:2020 | Volume:30 | Issue:184 Page(s): 106-118

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Title

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

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Keywords

quantitative structure–activity relationshipQ1

multiple linear regressionQ1

Bayesian regularized artificial neural networkQ1

Thiosemicarbazone derivativesQ1

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 threelayer 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.

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APA: Copy

MOUSAVI, MEHDI, Daryaee, Fereidoon, Ranjbaran, Omid, MOHSENI, BEHNAM, Taheri, Saeideh, & Hassanzadeh, Abdolreza. (2020). Quantitative Structure-Activity Relationship Study on Thiosemicarbazone Derivatives as Antitubercular agents Using Artificial Neural Network and Multiple Linear Regression. JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES, 30(184 ), 106-118. SID. https://sid.ir/paper/406688/en

Vancouver: Copy

MOUSAVI MEHDI, Daryaee Fereidoon, Ranjbaran Omid, MOHSENI BEHNAM, Taheri Saeideh, Hassanzadeh Abdolreza. Quantitative Structure-Activity Relationship Study on Thiosemicarbazone Derivatives as Antitubercular agents Using Artificial Neural Network and Multiple Linear Regression. JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2020;30(184 ):106-118. Available from: https://sid.ir/paper/406688/en

IEEE: Copy

MEHDI MOUSAVI, Fereidoon Daryaee, Omid Ranjbaran, BEHNAM MOHSENI, Saeideh Taheri, and Abdolreza Hassanzadeh, “Quantitative Structure-Activity Relationship Study on Thiosemicarbazone Derivatives as Antitubercular agents Using Artificial Neural Network and Multiple Linear Regression,” JOURNAL OF MAZANDARAN UNIVERSITY OF MEDICAL SCIENCES, vol. 30, no. 184 , pp. 106–118, 2020, [Online]. Available: https://sid.ir/paper/406688/en

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