A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach
Joint Authors
Adeniji, Shola Elijah
Uba, Sani
Uzairu, Adamu
Arthur, David Ebuka
Source
Advances in Preventive Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M.
tuberculosis.
Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive.
QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M.
tuberculosis.
This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach.
In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents.
The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s.
The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.9265, adjusted correlation coefficient (R2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv∧2) value of 0.8512, while the external validation test was found to have (R2 test) of 0.8034 and Y-randomization coefficient (cR_p∧2) of 0.6633.
The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents.
American Psychological Association (APA)
Adeniji, Shola Elijah& Uba, Sani& Uzairu, Adamu& Arthur, David Ebuka. 2019. A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach. Advances in Preventive Medicine،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1121795
Modern Language Association (MLA)
Adeniji, Shola Elijah…[et al.]. A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach. Advances in Preventive Medicine No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1121795
American Medical Association (AMA)
Adeniji, Shola Elijah& Uba, Sani& Uzairu, Adamu& Arthur, David Ebuka. A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach. Advances in Preventive Medicine. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1121795
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121795