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Computational Analysis of Artimisinin Derivatives on the Antitumor Activities

The study on antitumor activities of artemisinin and its derivatives has been closely focused on in recent years. Herein, 2D and 3D QSAR analysis was performed on the basis of a series of artemisinin derivatives with known bioactivities against the non-small-cell lung adenocarcinoma A549 cells. Four...

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Autores principales: Liu, Hui, Liu, Xingyong, Zhang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709249/
https://www.ncbi.nlm.nih.gov/pubmed/29094266
http://dx.doi.org/10.1007/s13659-017-0142-x
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author Liu, Hui
Liu, Xingyong
Zhang, Li
author_facet Liu, Hui
Liu, Xingyong
Zhang, Li
author_sort Liu, Hui
collection PubMed
description The study on antitumor activities of artemisinin and its derivatives has been closely focused on in recent years. Herein, 2D and 3D QSAR analysis was performed on the basis of a series of artemisinin derivatives with known bioactivities against the non-small-cell lung adenocarcinoma A549 cells. Four QSAR models were successfully established by CoMSIA, CoMFA, topomer CoMFA and HQSAR approaches with respective characteristic values q(2) = 0.567, R(2) = 0.968, ONC = 5; q(2) = 0.547, R(2) = 0.980, ONC = 7; q(2) = 0.559, R(2) = 0.921, ONC = 7 and q(2) = 0.527, R(2) = 0.921, ONC = 6. The predictive ability of CoMSIA with r(2) = 0.991 is the best one compared with the other three approaches, such as CoMFA (r(2) = 0.787), topomer CoMFA (r(2) = 0.819) and HQSAR (r(2) = 0.743). The final QSAR models can provide guidance in structural modification of artemisinin derivatives to improve their anticancer activities.
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spelling pubmed-57092492017-12-06 Computational Analysis of Artimisinin Derivatives on the Antitumor Activities Liu, Hui Liu, Xingyong Zhang, Li Nat Prod Bioprospect Original Article The study on antitumor activities of artemisinin and its derivatives has been closely focused on in recent years. Herein, 2D and 3D QSAR analysis was performed on the basis of a series of artemisinin derivatives with known bioactivities against the non-small-cell lung adenocarcinoma A549 cells. Four QSAR models were successfully established by CoMSIA, CoMFA, topomer CoMFA and HQSAR approaches with respective characteristic values q(2) = 0.567, R(2) = 0.968, ONC = 5; q(2) = 0.547, R(2) = 0.980, ONC = 7; q(2) = 0.559, R(2) = 0.921, ONC = 7 and q(2) = 0.527, R(2) = 0.921, ONC = 6. The predictive ability of CoMSIA with r(2) = 0.991 is the best one compared with the other three approaches, such as CoMFA (r(2) = 0.787), topomer CoMFA (r(2) = 0.819) and HQSAR (r(2) = 0.743). The final QSAR models can provide guidance in structural modification of artemisinin derivatives to improve their anticancer activities. Springer Singapore 2017-11-01 /pmc/articles/PMC5709249/ /pubmed/29094266 http://dx.doi.org/10.1007/s13659-017-0142-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Liu, Hui
Liu, Xingyong
Zhang, Li
Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title_full Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title_fullStr Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title_full_unstemmed Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title_short Computational Analysis of Artimisinin Derivatives on the Antitumor Activities
title_sort computational analysis of artimisinin derivatives on the antitumor activities
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709249/
https://www.ncbi.nlm.nih.gov/pubmed/29094266
http://dx.doi.org/10.1007/s13659-017-0142-x
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