Multi-linear regression model, molecular binding interactions and ligand-based design of some prominent compounds against Mycobacterium tuberculosis

Adeniji, Shola Elijah; Shallangwa, Gideon Adamu; Uzairu, Adamu

doi:10.1007/s13721-019-0212-6

Cited by 13 publications

(41 citation statements)

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“…The internal and external validation criteria for both test and training set reported were compared with the generally accepted threshold value for any QSAR model [11,[13][14][15] in order to affirm the reliability, fitting, stability, robustness and predictability of the developed models.…”

Section: Assertion Of the Build Modelmentioning

confidence: 99%

Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

et al. 2020

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Emergence of multi-drug resistant strains of Mycobacterium tuberculosis to the available drugs has demanded for the development of more potent anti-tubercular agents with efficient pharmacological activities. Time consumed and expenses in discovering and synthesizing new drug targets with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis. To solve the above problem, Quantitative Structure Activity Relationship (QSAR) is a recent approach developed to discover a novel drug with a better biological against M. Tuberculosis. A validated QSAR model developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors; AATS7s, VR1-Dzi, VR1-Dzs, SpMin7-Bhe and RDF110i. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.8875, adjusted correlation coefficient (R2adj) value of 0.8234 and leave one out cross validation coefficient (Qcv2) value of 0.8012 while the external validation test was found to have (R2test) of 0.7961 and Y-randomization Coefficient (cRp2) of 0.6832. Molecular docking shows that ligand 13 of 2,4-disubstituted quinoline derivatives have promising higher binding score of -18.8 kcal/mol compared to the recommended drugs; isoniazid -14.6 kcal/mol. The proposed QSAR model and molecular docking studies will provides valuable approach for the modification of the lead compound, designing and synthesis more potent anti-tubercular agents.

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Section: Assertion Of the Build Modelmentioning

confidence: 99%

Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

et al. 2020

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“…The prevalence of tuberculosis is rising all over the world due to population growth, aging, and an increase of physical inactivity. The current treatment options for tuberculosis include: rifampicin, pyrazinamide, para-amino salicylic acid and isoniazid [2]. However, reports have shown that patients do not respond positively to the administered drugs due to resistance strain of M. tuberculosis toward the current drugs.…”

Section: Introductionmentioning

confidence: 99%

“…However, reports have shown that patients do not respond positively to the administered drugs due to resistance strain of M. tuberculosis toward the current drugs. More also, most of these drugs have been reported with adverse side effect [2]. Therefore, the pursuit of novel antitubercular agents with enhanced and efficient properties/ activities with minima side effects against M. tuberculosis still remains a challenge to pharmacist and chemists [3].…”

Section: Introductionmentioning

confidence: 99%

“…Triazole compounds are the subject of many research studies due to its structural entities and their widespread potential in medicinal applications [2,6]. Triazole and its analogue among all other heterocyclic compounds are being considered in pharmacological fields due to its unique structure and properties [3,6].…”

Section: Introductionmentioning

confidence: 99%

“…Computational chemistry has make in-silico methods to become widely used in the field of structure-based drug design which reduces the cost for effective evaluation of large virtual database of chemical compounds. Such computational method includes Quantitative Structure-Activity Relationships (QSAR) and molecular docking [2].…”

Section: Introductionmentioning

confidence: 99%

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Genetic functional algorithm model, docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis

Adeniji

2020

Egyptian Journal of Basic and Applied Sciences

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Tuberculosis still persist a major challenge to healthcare system around the world. Increasing rates of morbidity, mortality and the reoccurrence of the resistant strains of the disease to accessible drugs/medications have made it crucial to search for a new therapy in term of novel and prominent hypothetical compound treat tuberculosis. Therefore, Quantitative Structure-Activity Relationship (QSAR) was employed as a modelling technique to predict and design new compounds with improved and efficient biological activities against Mycobacterium tuberculosis. To accomplish the purpose of this work, Genetic function approximation was adopted to derive the model. The established model was swayed with topological descriptors; MATS9s, SM1_DzZ, SpMin4_Bhv, TDB2v and RDF70v.More also, interactions between the compounds and the target protein ''DNA gyrase'' indicated that compound 20 has the most perceptible binding affinity of-18.12kcal/mol. Consequently, compound 20 served as a reference structural template and insight to design fourteen novel hypothetical agents with more prominent anti-tubercular activities. More also, compound 20j was observed with high activity among the designed compounds with more a prominent binding affinities of-24.3 kcal/mol. Therefore this research recommends in-vivo, in-vitro screening and pharmacokinetic properties to be carried out in order to determine the toxicity of the designed compounds.

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A Hybrid Atmospheric Satellite Image-Processing Method for Dust and Horizontal Visibility Detection through Feature Extraction and Machine Learning Techniques

Amiri

Soleimani

2022

J Indian Soc Remote Sens

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Multi-linear regression model, molecular binding interactions and ligand-based design of some prominent compounds against Mycobacterium tuberculosis

Cited by 13 publications

References 5 publications

Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

Theoretical investigation of a few selected compounds as potent anti-tubercular agents and molecular docking evaluation: A multi-linear regression approach

Genetic functional algorithm model, docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis

A Hybrid Atmospheric Satellite Image-Processing Method for Dust and Horizontal Visibility Detection through Feature Extraction and Machine Learning Techniques

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