Thiazolidine derivatives anticancer activity prediction using RDF molecular descriptors
Keywords:
QSAR analysis, RDF molecular descriptors, virtual screening, thiazolidine derivatives
Abstract
Research of «structure — anticancer activity» dependence for 17 thiazolidine derivatives, which posses high selectivity of anticancer action on lines 786-O and UO-31 of kidney cancer cells, using RDF 3D molecular descriptors was carried out.
Using statistical analysis of obtained regression equations we can make an assumption about dependence of anticancer activity on sizes and 3D configurations of molecules and about different mechanisms of action on certain cancer cell lines.
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3. Carter P.H., Scherle P.A., Muckelbauer J.A. et al. // Proc. Natl. Acad. Sci. U.S.A. - 2001. - Vol. 98 - P. 11879-11886.
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6. DRAGON web version 3.0. QSAR software developed by Milano Chemometrics and QSAR Research Group, Department of Scienze dell'Ambiente e del Territorio Universita degli Studi di Milano - Bicocca.
7. Ekins S., Mestres J., Testa B. // British J. of Pharmacology. - 2007. - Vol. 152 No. 1. P. 9-20.
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Published
2019-10-29
How to Cite
Myrko, I., Ogurtsov, V., & Kaminskiy, D. (2019). Thiazolidine derivatives anticancer activity prediction using RDF molecular descriptors. Farmatsevtychnyi Zhurnal, (4), 50-54. Retrieved from https://pharmj.org.ua/index.php/journal/article/view/777
Section
Original Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
