THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH
A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2019-03-01
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| Series: | The Journal of Engineering and Exact Sciences |
| Subjects: | |
| Online Access: | https://periodicos.ufv.br/ojs/jcec/article/view/2509 |
| _version_ | 1819118428836855808 |
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| author | Shola Elijah Adeniji Momohjimoh Idris Ovaku Tukur Saidu Ahanonu Saviour Ugochukwu Gideon Shallangwa Adamu Uzairu |
| author_facet | Shola Elijah Adeniji Momohjimoh Idris Ovaku Tukur Saidu Ahanonu Saviour Ugochukwu Gideon Shallangwa Adamu Uzairu |
| author_sort | Shola Elijah Adeniji |
| collection | DOAJ |
| description | A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds. |
| first_indexed | 2024-12-22T05:48:44Z |
| format | Article |
| id | doaj.art-4fd0f084daf746a289e80d4ee84a675d |
| institution | Directory Open Access Journal |
| issn | 2527-1075 |
| language | English |
| last_indexed | 2024-12-22T05:48:44Z |
| publishDate | 2019-03-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj.art-4fd0f084daf746a289e80d4ee84a675d2022-12-21T18:36:57ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752019-03-01510125013610.18540/jcecvl5iss1pp0125-01361968THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACHShola Elijah Adeniji0Momohjimoh Idris OvakuTukur SaiduAhanonu Saviour UgochukwuGideon ShallangwaAdamu UzairuAhmadu Bello University, Zaria, NigeriaA Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds.https://periodicos.ufv.br/ojs/jcec/article/view/2509Ciprofloxacin, Descriptor, Genetic Function Approximation, Lung Cancer, QSAR. |
| spellingShingle | Shola Elijah Adeniji Momohjimoh Idris Ovaku Tukur Saidu Ahanonu Saviour Ugochukwu Gideon Shallangwa Adamu Uzairu THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH The Journal of Engineering and Exact Sciences Ciprofloxacin, Descriptor, Genetic Function Approximation, Lung Cancer, QSAR. |
| title | THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
| title_full | THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
| title_fullStr | THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
| title_full_unstemmed | THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
| title_short | THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
| title_sort | theoretical modelling for investigating some active compounds as potent inhibitors against lung cancer a multi linear regression approach |
| topic | Ciprofloxacin, Descriptor, Genetic Function Approximation, Lung Cancer, QSAR. |
| url | https://periodicos.ufv.br/ojs/jcec/article/view/2509 |
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