QSAR-АНАЛІЗ АФІНІТЕТУ РЯДУ ЕКДИСТЕРОЇДІВ НА ОСНОВІ 2.5D-СИМПЛЕКСНОГО ПРЕДСТАВЛЕННЯ МОЛЕКУЛЯРНОЇ СТРУКТУРИ

A. Mouats, A. G. Artemenko, O. P. Lebed, V. A. Shapkin, V. E. Kuz’min

Анотація


Розроблено та верифіковано розширення симплексного представлення молекулярної структури, яке дозволяє вирішувати задачі «структура-властивість» для хіральних сполук. На його основі отримано адекватні QSAR-моделі афінітету екдистероїдів до екдизонового рецептору. Показано вплив фізико-хімічних чинників і деяких структурних фрагментів.

Ключові слова


QSAR; екдистероїди; хіральність; симплексне представлення молекулярної структури

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Пристатейна бібліографія ГОСТ


1. Seel M., Turner D.B., Wilett P. Effect of Parameter Variations on the Effectiveness of HQSAR Analyses // QSAR. – 1999. – Vol. 18, No. 3. – P. 245–252. http://dx.doi.org/10.1002/(sici)1521-3838(199907)18:3<245::aidqsar245>3.0.co;2-o


2. Сухачев Д.В., Пивина T.С., Шляпочников В.А., Петров Э.А., Палюлин В.А, Зефиров Н.С.Исследование количественных соотношений «структура-чувствительность к удару» органических полиазотистых веществ // Доклады РАН. – 1993. – Т. 328, № 2. – С. 50–57.


3. Cramer, R.D., Patterson, D.I.& Bunce, J.D. Comparative Molecular Field Analysis (CoMFA). 1.Effect of Shape Binding to Carrier Proteins // J. Am. Chem. Soc. – 1988. – Vol. 110, No. 18. – P. 5959–5967. http://dx.doi.org/10.1021/ja00226a005


4. Silverman B.D. The Thirty-one Benchmark Steroids Revisited: Comparative Molecular Moment Analysis (CoMMA) with Principal Component Regression // Quantitative Structure-Activity Relationships. – 2000. – Vol. 19, No. 3. – P. 237–246. http://dx.doi.org/10.1002/1521-3838(200006)19:3<237::aid-qsar237>3.3.co;2-1


5. Klebe G., Abraham U., Mietzner T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity. // J. Med. Chem. – 1994. – Vol. 37, No. 24. – P. 4130–4146. http://dx.doi.org/10.1021/jm00050a010


6. Stiefl N., Baumann K. Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique. // J. Med. Chem. – 2003. – Vol. 46, No. 8. – P. 1390–1407. http://dx.doi.org/10.1021/jm021077w


7. Dinan L., Hormann R.E, Fujimoto T. An extensive ecdysteroid CoMFA // Journal of Computer-Aided Molecular Design. – 1999. – Vol. 13, No. 2. – P.185–207. http://dx.doi.org/10.1023/a:1008052320014


8. So S.S., Karplus M. Three-Dimensional Quantitative Structure-Activity Relationships from Molecular Similarity Matrices and Genetic Neural Networks. 1. Method and Validations. // J. Med. Chem. – 1997. – Vol. 40, No.26. – P. 4347–4359. http://dx.doi.org/10.1021/jm970487v

9. Marrero-Ponce Y., Castillo-Garit J.A., Castro E.A., Torrens F.,· Rotondo R 3D-chiral (2.5) atom-based TOMOCOMD-CARDD descriptors: theory and QSAR applications to central chirality codification // J Math Chem –2008. – Vol. 44, No. 3. – P. 755–786 http://dx.doi.org/10.1007/s10910-008-9386-3


10. Liu S.S., Yin C.S, Wang L.S. Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides,
and COX-2 inhibitors. // J. Chem. Inf. And Mod., – 2002. – Vol. 42, No. 3. – P. 749–756. http://dx.doi.org/10.1021/ci010245a


11. Lobato M., Amat L., Besalu E., Carbo-Dorca R. Structure‐activity relationships of a steroid family using quantum similarity measures and topological quantum similarity indices. // QSAR. – 1997. – Vol. 16, No. 6. – P.465–472. http://dx.doi.org/10.1002/qsar.19970160605


12. Parretti M.F., Kroemer R.T., Rothman J.H., Richards W.G. Alignment of molecules by the Monte Carlo optimization
of molecular similarity indices. // J. Comput. Chem. – 1997. – Vol. 18, No. 11. – P. 1334–1353. http://dx.doi.org/10.1002/(sici)1096-987x(199708)18:11%3C1344::aid-jcc2%3E3.0.co;2-l


13. Golbraikh A., Bonchev D., Tropsha A.. Novel Chirality Descriptors Derived from Molecular Topology // J.Chem. Inf. Comput. Sci. – 2001. – Vol. 41, No. 1. – P. 147–158. http://dx.doi.org/10.1021/ci000082a


14. Кузьмин В.Е., Артеменко А.Г, Муратов Н.Н. 4D–QSAR на основе симплексного представления молекулярной структуры. // Труды Одесского Политехнического Университета. – 2002. – № 2. – С. 219–223.


15. Rännar S., Lindgren F., Geladi P., Wold S. A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer objects. Part 1: Theory and Algorithm // J. Chemometrics. – 1994. – Vol. 8, No. 2. – P. 111–125. http://dx.doi.org/10.1002/cem.1180080204.


16. Gorb L., Kuz’min V., Muratov E. Application of Computationa; Techniques in Pharmacy and Medicine. – New York, Springer, 2014. – P. 549.


17. Polischuk P.G., Muratov E.N., Artemenko A.G., Kolumbin O.G., Muratov N.N., Kuz’min V.E. Universal Approach for Structural Interpretation of QSAR/ QSPR Models. // J. Chem Inf. Model. – 2009. – Vol. 49. – P. 2481–2488. http://dx.doi.org/10.1021/ci900203n





DOI: https://doi.org/10.18524/2304-0947.2016.1(57).67514

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