DOI: https://doi.org/10.18524/2304-0947.2016.1(57).67514
QSAR-АНАЛІЗ АФІНІТЕТУ РЯДУ ЕКДИСТЕРОЇДІВ НА ОСНОВІ 2.5D-СИМПЛЕКСНОГО ПРЕДСТАВЛЕННЯ МОЛЕКУЛЯРНОЇ СТРУКТУРИ
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Seel M., Turner D.B., Wilett P. Effect of Parameter Variations on the Effectiveness of HQSAR Analyses. QSAR, 1999, vol. 18, no. 3, pp. 245–252. http://dx.doi.org/10.1002/(sici)1521-3838(199907)18:3<245::aidqsar245>
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Suhachev D.V, Pivina T.S, Shlyapochnikov V.A, Petrov E.A., Palyulin V.A, Zefirov N.S. Issledovanie kolichestvennykh
sootnosheniy «struktura-chuvstvitel’nost’ k udaru organicheskikh polyazotistykh veshchestv.[Research of the quantitative relationships «structure – shock sensitivity» of the organic polyasotic compounds]. Doklady RAN, 1993, vol. 328, no. 2, pp. 50–57.
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, pp. 5959–5967. http://dx.doi.org/10.1021/ja00226a005
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, pp. 237–246. http://dx.doi.org/10.1002/1521-3838(200006)19:3<237::aid-qsar237>3.3.co;2-1
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, pp. 4130–4146. http://dx.doi.org/10.1021/jm00050a010
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, pp. 1390–1407. http://dx.doi.org/10.1021/jm021077w
Dinan L., Hormann R.E, Fujimoto T. An extensive ecdysteroid CoMFA. Journal of Computer-Aided Molecular Design, 1999, vol. 13, no. 2, pp. 185–207. http://dx.doi.org/10.1023/a:1008052320014
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, pp.4347–4359. http://dx.doi.org/10.1021/jm970487v
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, pp. 755–786. http://dx.doi.org/10.1007/s10910-008-9386-3
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. Comput. Sci., 2002, vol. 42, no. 3, pp. 749–756. http://dx.doi.org/10.1021/ci010245a
Lobato M., Amat L., Besalu E., Carbo-Dorca R. Structure‐activity relationships of a steroid family using quantum
similarity measures and topological quantum similarty indices. QSAR, 1997, vol. 16, no. 6, pp. 465–472. http://dx.doi.org/10.1002/qsar.19970160605
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, pp. 1334–1353. http://dx.doi.org/10.1002/(sici)1096-987x(199708)18:11%3C1344::aid-jcc2%3E3.0.co;2-l
Golbraikh A., Bonchev D., Tropsha A. Novel Chirality Descriptors Derived from Molecular Topology. J. Chem.
Inf. Comput. Sci., 2001, vol. 41, no. 1, pp. 147–158. http://dx.doi.org/10.1021/ci000082a
Kuz’min V.E., Artemenko A.G., Muratov N.N. 4D–QSAR na osnove simpleksnogo predstavleniya molekulyarnoy
struktury. [4D-QSAR based on simplex representation of moleculat structure]. Trudy Odesskogo Politehnicheskogo
Universiteta. 2002, no. 2, pp. 219–223.
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, pp. 111–125. http://dx.doi.org/10.1002/cem.1180080204
Gorb L., Kuz’min V., Muratov E. Application of Computational Techniques in Pharmacy and Medicine. New York, Springer, 2014, 549p.
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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

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