QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods
Quantitative structure-activity relationship (QSAR) is the most extensively used computational methodology for analogue-based design. In this research, QSAR model was used to predict antiproliferative properties of 4-(2-fluorophenoxy) quinoline derivatives against A549(human lung adenocarcinoma). Fo...
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| Format: | Article |
| Language: | English |
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Iranian Chemical Science and Technologies Association
2019-11-01
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| Series: | Chemical Review and Letters |
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| Online Access: | http://www.chemrevlett.com/article_103559_c1335f2635a9e2498ca321bd5e7b0e99.pdf |
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| author | Somayeh Alimohammadi Aliasghar Hamidi Parinaz Pargolghasemi Nasim Nourani Mir Saleh Hoseininezhad-Namin |
| author_facet | Somayeh Alimohammadi Aliasghar Hamidi Parinaz Pargolghasemi Nasim Nourani Mir Saleh Hoseininezhad-Namin |
| author_sort | Somayeh Alimohammadi |
| collection | DOAJ |
| description | Quantitative structure-activity relationship (QSAR) is the most extensively used computational methodology for analogue-based design. In this research, QSAR model was used to predict antiproliferative properties of 4-(2-fluorophenoxy) quinoline derivatives against A549(human lung adenocarcinoma). For this purpose, we used the multiple linear regressions (MLR) for the construction of a model to predict drug activity and Stepwise (SW) and genetic algorithm (GA) methods used to build the model. The data were selected from 31 molecules with specific pharmacological activity. They were divided into two sets train and test data. The resulting model was tested using statistical methods such as external test set and cross-validation to ensure its authenticity. The results showed that GA-MLR approach had good predictive power and higher data rates compared with SW-MLR (Q2LOO = 0.877, R2Train =0.933). The results obtained in this study can be used to design drugs with higher performance and pharmacological activity to treat lung cancer. |
| first_indexed | 2024-12-24T01:54:07Z |
| format | Article |
| id | doaj.art-07c7868dd3614aebba72ec94a54b3d26 |
| institution | Directory Open Access Journal |
| issn | 2676-7279 2645-4947 |
| language | English |
| last_indexed | 2024-12-24T01:54:07Z |
| publishDate | 2019-11-01 |
| publisher | Iranian Chemical Science and Technologies Association |
| record_format | Article |
| series | Chemical Review and Letters |
| spelling | doaj.art-07c7868dd3614aebba72ec94a54b3d262022-12-21T17:21:37ZengIranian Chemical Science and Technologies AssociationChemical Review and Letters2676-72792645-49472019-11-012419319810.22034/crl.2020.220465.1037103559QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methodsSomayeh Alimohammadi0Aliasghar Hamidi1Parinaz Pargolghasemi2Nasim Nourani3Mir Saleh Hoseininezhad-Namin4Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranBiotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, IranDepartment of Chemistry, Payame Noor University (PNU), P. O. Box, 19395-3697 Tehran, IranBiotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, IranBiotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, IranQuantitative structure-activity relationship (QSAR) is the most extensively used computational methodology for analogue-based design. In this research, QSAR model was used to predict antiproliferative properties of 4-(2-fluorophenoxy) quinoline derivatives against A549(human lung adenocarcinoma). For this purpose, we used the multiple linear regressions (MLR) for the construction of a model to predict drug activity and Stepwise (SW) and genetic algorithm (GA) methods used to build the model. The data were selected from 31 molecules with specific pharmacological activity. They were divided into two sets train and test data. The resulting model was tested using statistical methods such as external test set and cross-validation to ensure its authenticity. The results showed that GA-MLR approach had good predictive power and higher data rates compared with SW-MLR (Q2LOO = 0.877, R2Train =0.933). The results obtained in this study can be used to design drugs with higher performance and pharmacological activity to treat lung cancer.http://www.chemrevlett.com/article_103559_c1335f2635a9e2498ca321bd5e7b0e99.pdflung cancerquinoline derivativemultiple linear regressionsgenetic algorithm |
| spellingShingle | Somayeh Alimohammadi Aliasghar Hamidi Parinaz Pargolghasemi Nasim Nourani Mir Saleh Hoseininezhad-Namin QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods Chemical Review and Letters lung cancer quinoline derivative multiple linear regressions genetic algorithm |
| title | QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods |
| title_full | QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods |
| title_fullStr | QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods |
| title_full_unstemmed | QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods |
| title_short | QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods |
| title_sort | qsar study of antiproliferative drug against a549 by ga mlr and sw mlr methods |
| topic | lung cancer quinoline derivative multiple linear regressions genetic algorithm |
| url | http://www.chemrevlett.com/article_103559_c1335f2635a9e2498ca321bd5e7b0e99.pdf |
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