Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach
Tuberculosis (TB) is of one the most infectious disease caused by Mycobacterium tuberculosis which remains a serious public health problem. Emergence of multi-drug resistant strains of M. tuberculosis led to development of new and more potent anti-tuberculosis agents. The aim of this study was to co...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-01-01
|
| Series: | Journal of King Saud University: Science |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364718303549 |
| _version_ | 1828518307879190528 |
|---|---|
| author | Shola Elijah Adeniji Sani Uba Adamu Uzairu |
| author_facet | Shola Elijah Adeniji Sani Uba Adamu Uzairu |
| author_sort | Shola Elijah Adeniji |
| collection | DOAJ |
| description | Tuberculosis (TB) is of one the most infectious disease caused by Mycobacterium tuberculosis which remains a serious public health problem. Emergence of multi-drug resistant strains of M. tuberculosis led to development of new and more potent anti-tuberculosis agents. The aim of this study was to correlate the chemical structures of the inhibitory compounds with their experimental activities. In this study, analogs of 2,4-disubstituted quinoline derivatives as potent anti-tubercular agents was subjected to quantitative structure–activity relationship (QSAR) analysis in order to build a QSAR model for predicting the activities of these compounds. In order to build the regression model, Genetic Function Approximation (GFA) and Multi-linear Regression approach were used to predict the activities of inhibitory compounds. Based on the prediction, the best validation model was found to have squared correlation coefficient (R2) of 0.9367, adjusted squared correlation coefficient (R2 adj) value of 0.9223 and cross validation coefficient (Qcv2) value of 0.8752. The chosen model was subjected to external validations and the model was found to have (R2 test) of 0.8215 and Y-randomization Coefficient (cRp2) of 0.6633. The proposed QSAR model provides a valuable approach for designing more potent anti-tubercular agents. Keywords: Anti-tuberculosis, Applicability domain, Genetic Function Algorithm, Multi-linear Regression, QSAR |
| first_indexed | 2024-12-11T18:53:27Z |
| format | Article |
| id | doaj.art-a896a0de98e04d079ead81c417dc8ffe |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-12-11T18:53:27Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-a896a0de98e04d079ead81c417dc8ffe2022-12-22T00:54:14ZengElsevierJournal of King Saud University: Science1018-36472020-01-01321575586Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approachShola Elijah Adeniji0Sani Uba1Adamu Uzairu2Corresponding author.; Department of Chemistry, Ahmadu Bello University, Zaria, NigeriaDepartment of Chemistry, Ahmadu Bello University, Zaria, NigeriaDepartment of Chemistry, Ahmadu Bello University, Zaria, NigeriaTuberculosis (TB) is of one the most infectious disease caused by Mycobacterium tuberculosis which remains a serious public health problem. Emergence of multi-drug resistant strains of M. tuberculosis led to development of new and more potent anti-tuberculosis agents. The aim of this study was to correlate the chemical structures of the inhibitory compounds with their experimental activities. In this study, analogs of 2,4-disubstituted quinoline derivatives as potent anti-tubercular agents was subjected to quantitative structure–activity relationship (QSAR) analysis in order to build a QSAR model for predicting the activities of these compounds. In order to build the regression model, Genetic Function Approximation (GFA) and Multi-linear Regression approach were used to predict the activities of inhibitory compounds. Based on the prediction, the best validation model was found to have squared correlation coefficient (R2) of 0.9367, adjusted squared correlation coefficient (R2 adj) value of 0.9223 and cross validation coefficient (Qcv2) value of 0.8752. The chosen model was subjected to external validations and the model was found to have (R2 test) of 0.8215 and Y-randomization Coefficient (cRp2) of 0.6633. The proposed QSAR model provides a valuable approach for designing more potent anti-tubercular agents. Keywords: Anti-tuberculosis, Applicability domain, Genetic Function Algorithm, Multi-linear Regression, QSARhttp://www.sciencedirect.com/science/article/pii/S1018364718303549 |
| spellingShingle | Shola Elijah Adeniji Sani Uba Adamu Uzairu Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach Journal of King Saud University: Science |
| title | Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach |
| title_full | Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach |
| title_fullStr | Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach |
| title_full_unstemmed | Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach |
| title_short | Theoretical modeling for predicting the activities of some active compounds as potent inhibitors against Mycobacterium tuberculosis using GFA-MLR approach |
| title_sort | theoretical modeling for predicting the activities of some active compounds as potent inhibitors against mycobacterium tuberculosis using gfa mlr approach |
| url | http://www.sciencedirect.com/science/article/pii/S1018364718303549 |
| work_keys_str_mv | AT sholaelijahadeniji theoreticalmodelingforpredictingtheactivitiesofsomeactivecompoundsaspotentinhibitorsagainstmycobacteriumtuberculosisusinggfamlrapproach AT saniuba theoreticalmodelingforpredictingtheactivitiesofsomeactivecompoundsaspotentinhibitorsagainstmycobacteriumtuberculosisusinggfamlrapproach AT adamuuzairu theoreticalmodelingforpredictingtheactivitiesofsomeactivecompoundsaspotentinhibitorsagainstmycobacteriumtuberculosisusinggfamlrapproach |