Startseite MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity

  • Edward Seclaman EMAIL logo , Alina Bora , Sorin Avram , Zeno Simon und Ludovic Kurunczi
Veröffentlicht/Copyright: 21. Mai 2011
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

A series of 36 substituted 2-phenylindoles was analysed using minimal topological difference-projections in latent structures variant (MTD-PLS) and molecular docking, using fast rigid exhaustive docking (FRED) and AutoDock Vina programs. For quantitative structure activity relationships (QSAR) validation, a sphere exclusion algorithm in the multi-dimensional descriptor space was used to construct training and test sets. Docking procedures were based on X-ray crystallography studies using the human alpha oestrogen receptor-17β-oestradiol complex. The ranking abilities of the different scoring functions of the FRED package were presented, and the most suitable scoring function (Chemgauss3) for the oestrogen receptor was chosen. Although the series studied contains only a limited number of compounds, the MTD-PLS method and the docking procedure provided coherent results in concordance with the X-ray diffraction data for different ligand-oestrogen receptor complexes.

[1] Balakrishnan, N., & Lai, C.-D. (2009). Continuous bivariate distributions (2nd ed., pp. 141–173). New York, NY, USA: Springer. http://dx.doi.org/10.1007/b101765_510.1007/b101765_5Suche in Google Scholar

[2] Bhatia, N. M., Mahadik, K. R., & Bhatia, M. S. (2009). QSAR analysis of 1,3-diaryl-2 propen-1-ones and their indole analogs for designing potent antibacterial agents. Chemical Papers, 63, 456–463. DOI: 10.2478/s11696-009-0026-6. http://dx.doi.org/10.2478/s11696-009-0026-610.2478/s11696-009-0026-6Suche in Google Scholar

[3] Böhm, H.-J. (1994). The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. Journal of Computer-Aided Molecular Design, 8, 243–256. DOI: 10.1007/BF00126743. http://dx.doi.org/10.1007/BF0012674310.1007/BF00126743Suche in Google Scholar

[4] Boström, J., Greenwood, J. R., & Gottfries, J. (2003). Assessing the performance of OMEGA with respect to retrieving bioactive conformations. Journal of Molecular Graphics and Modelling, 21, 449–462. DOI: 10.1016/S1093-3263(02)00204-8. http://dx.doi.org/10.1016/S1093-3263(02)00204-810.1016/S1093-3263(02)00204-8Suche in Google Scholar

[5] Brzozowski, A. M., Pike, A. C. W., Dauter, Z., Hubbard, R. E., Bonn, T., Engström, O., Öhman, L., Greene, G. L., Gustafsson, J.-Å., & Carlquist, M. (1997). Molecular basis of agonism and antagonism in the oestrogen receptor. Nature, 389, 753–758. DOI: 10.1038/39645. http://dx.doi.org/10.1038/3964510.1038/39645Suche in Google Scholar

[6] Coleman, K. P., Toscano, W. A., Jr., & Wiese, T. E. (2003). QSAR models of the in vitro estrogen activity of bisphenol A analogs. QSAR & Combinatorial Science, 22, 78–88. DOI: 10.1002/qsar.200390008. http://dx.doi.org/10.1002/qsar.20039000810.1002/qsar.200390008Suche in Google Scholar

[7] Cronin, M. T. D., & Schultz, T. W. (2003). Pitfalls in QSAR. Journal of Molecular Structure: THEOCHEM, 622, 39–51. DOI: 10.1016/S0166-1280(02)00616-4. http://dx.doi.org/10.1016/S0166-1280(02)00616-410.1016/S0166-1280(02)00616-4Suche in Google Scholar

[8] Ding, D., Xu, L., Fang, H., Hong, H., Perkins, R., Harris, S., Bearden, E. D., Shi, L., & Tong, W. (2010). The EDKB: an established knowledge base for endocrine disrupting chemicals. BMC Bioinformatics, 11(Suppl. 6), S5. DOI: 10.1186/1471-2105-11-S6-S5. http://dx.doi.org/10.1186/1471-2105-11-S6-S510.1186/1471-2105-11-S6-S5Suche in Google Scholar PubMed PubMed Central

[9] Ferrara, P., Gohlke, H., Price, D. J., Klebe, G., & Brooks, C. L., III (2004). Assessing scoring functions for protein-ligand interactions. Journal of Medicinal Chemistry, 47, 3032–3047. DOI: 10.1021/jm030489h. http://dx.doi.org/10.1021/jm030489h10.1021/jm030489hSuche in Google Scholar PubMed

[10] Gao, H., Katzenellenbogen, J. A., Garg, R., & Hansch, C. (1999). Comparative QSAR analysis of estrogen receptor ligands. Chemical Reviews, 99, 723–744. DOI: 10.1021/cr9800 18g. http://dx.doi.org/10.1021/cr980018gSuche in Google Scholar

[11] Golbraikh, A. (2000). Molecular dataset diversity indices and their applications to comparison of chemical databases and QSAR analysis. Journal of Chemical Information and Computer Sciences, 40, 414–425. DOI: 10.1021/ci990437u. 10.1021/ci990437uSuche in Google Scholar PubMed

[12] Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17, 241–253. DOI: 10.1023/A:1025386326946. http://dx.doi.org/10.1023/A:102538632694610.1023/A:1025386326946Suche in Google Scholar

[13] Golbraikh, A., & Tropsha, A. (2002a). Beware of q 2! Journal of Molecular Graphics and Modelling, 20, 269–276. DOI: 10.1016/S1093-3263(01)00123-1. http://dx.doi.org/10.1016/S1093-3263(01)00123-110.1016/S1093-3263(01)00123-1Suche in Google Scholar

[14] Golbraikh, A., & Tropsha, A. (2002b). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Journal of Computer-Aided Molecular Design, 16, 357–369. DOI: 10.1023/A: 1020869118689. http://dx.doi.org/10.1023/A:102086911868910.1023/A:1020869118689Suche in Google Scholar

[15] Golob, T., Liebl, R., & von Angerer, E. (2002). Sulfamoyloxysubstituted 2-phenylindoles: Antiestrogen-based inhibitors of the steroid sulfatase in human breast cancer cells. Bioorganic & Medicinal Chemistry, 10, 3941–3953. DOI: 10.1016/S0968-0896(02)00306-1. http://dx.doi.org/10.1016/S0968-0896(02)00306-110.1016/S0968-0896(02)00306-1Suche in Google Scholar

[16] Hypercube, Inc. (2005). HyperChem, Version 7.52 for Windows [computer software]. Gainesville, FL, USA: Hypercube Inc. Suche in Google Scholar

[17] Jain, A. N., & Nicholls, A. (2008). Recommendations for evaluation of computational methods. Journal of Computer-Aided Molecular Design, 22, 133–139. DOI: 10.1007/s10822-008-9196-5. http://dx.doi.org/10.1007/s10822-008-9196-510.1007/s10822-008-9196-5Suche in Google Scholar PubMed PubMed Central

[18] Katzenellenbogen, J. A., Muthyala, R., & Katzenellenbogen, B. S. (2003). Nature of the ligand-binding pocket of estrogen receptor α and β: The search for subtype-selective ligands and implications for the prediction of estrogenic activity. Pure and Applied Chemistry, 75, 2397–2403. DOI: 10.1351/pac200375112397. http://dx.doi.org/10.1351/pac20037511239710.1351/pac200375112397Suche in Google Scholar

[19] Kiss, G., & Allen, N. W. (2007). Automated docking of estrogens and SERMs into an estrogen receptor alpha and beta isoform using the PMF forcefield and the Lamarckian genetic algorithm. Theoretical Chemistry Accounts, 117, 305–314. DOI: 10.1007/s00214-006-0138-9. http://dx.doi.org/10.1007/s00214-006-0138-910.1007/s00214-006-0138-9Suche in Google Scholar

[20] Knox, A. J. S., Meegan, M. J., Sobolev, V., Frost, D., Zisterer, D. M., Williams, D. C., & Lloyd, D. G. (2007). Target specific virtual screening: Optimization of an estrogen receptor screening platform. Journal of Medicinal Chemistry, 50, 5301–5310. DOI: 10.1021/jm0700262. http://dx.doi.org/10.1021/jm070026210.1021/jm0700262Suche in Google Scholar PubMed

[21] Kurunczi, L., Seclaman, E., Oprea, T. I., Crisan, L., & Simon, Z. (2005). MTD-PLS: A PLS variant of the minimal topologic difference method. III. Mapping interactions between estradiol derivatives and the alpha estrogenic receptor. Journal of Chemical Information and Modeling, 45, 1275–1281. DOI: 10.1021/ci050077c. http://dx.doi.org/10.1021/ci050077c10.1021/ci050077cSuche in Google Scholar PubMed

[22] Li, H., Ung, C. Y., Yap, C. W., Xue, Y., Li, Z. R., & Chen, Y. Z. (2006). Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. Journal of Molecular Graphics and Modelling, 25, 313–323. DOI: 10.1016/j.jmgm.2006.01.007. http://dx.doi.org/10.1016/j.jmgm.2006.01.00710.1016/j.jmgm.2006.01.007Suche in Google Scholar PubMed

[23] Liu, H., Papa, E., & Gramatica, P. (2008). Evaluation and QSAR modeling on multiple endpoints of estrogen activity based on different bioassays. Chemosphere, 70, 1889–1897. DOI: 10.1016/j.chemosphere.2007.07.071. http://dx.doi.org/10.1016/j.chemosphere.2007.07.07110.1016/j.chemosphere.2007.07.071Suche in Google Scholar PubMed

[24] Marini, F., Roncaglioni, A., & Novič, M. (2005). Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders. Journal of Chemical Information and Modeling, 45, 1507–1519. DOI: 10.1021/ci0501645. http://dx.doi.org/10.1021/ci050164510.1021/ci0501645Suche in Google Scholar PubMed

[25] Memminger, M. (2009). Synthesis and characterization of subtype-selective estrogen receptor ligands and their application as pharmacological tools. Doctoral dissertation, University of Regensburg, Regensburg, Germany. Suche in Google Scholar

[26] Moitessier, M., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008). Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. British Journal of Pharmacology, 153, S7–S26. DOI: 10.1038/sj.bjp.0707515. http://dx.doi.org/10.1038/sj.bjp.070751510.1038/sj.bjp.0707515Suche in Google Scholar PubMed PubMed Central

[27] Nemeček, P., Ďurčeková, T., Mocák, J., & Waisser, K. (2009). Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction. Chemical Papers, 63, 84–91. DOI: 10.2478/s11696-008-0089-9. http://dx.doi.org/10.2478/s11696-008-0089-910.2478/s11696-008-0089-9Suche in Google Scholar

[28] Nettles, K. W., Bruning, J. B., Gil, G., Nowak, J., Sharma, S. K., Hahm, J. B., Kulp, K., Hochberg, R. B., Zhou, H., Katzenellenbogen, J. A., Katzenellenbogen, B. S., Kim, Y., Joachimiak, A., & Greene, G. L. (2008). NFκB selectivity of estrogen receptor ligands revealed by comparative crystallographic analyses. Nature Chemical Biology, 4, 241–247. DOI: 10.1038/nchembio.76. http://dx.doi.org/10.1038/nchembio.7610.1038/nchembio.76Suche in Google Scholar PubMed PubMed Central

[29] Olah, M. (2000). Molecular fragment volume calculation for QSAR studies. Revue Roumaine de Chimie, 45, 1123–1125. Suche in Google Scholar

[30] OpenEye Scientific Software, Inc. (2009). FRED, Version 2.2.5. Santa Fe, NM, USA: OpenEye Scientific Software, Inc. Suche in Google Scholar

[31] OpenEye Scientific Software, Inc. (2008). OMEGA, Version 2.3.2. Santa Fe, NM, USA: OpenEye Scientific Software, Inc. Suche in Google Scholar

[32] Oprea, T. I., Kurunczi, L., Olah, M., & Simon, Z. (2001). MTD-PLS: A PLS-based variant of the MTD method. A 3D-QSAR analysis of receptor affinities for a series of halogenated dibenzoxin and biphenyl derivatives. SAR and QSAR in Environmental Research, 12, 75–92. DOI: 10.1080/10629360108035372. http://dx.doi.org/10.1080/1062936010803537210.1080/10629360108035372Suche in Google Scholar PubMed

[33] Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25, 1605–1612. DOI: 10.1002/jcc.20084. http://dx.doi.org/10.1002/jcc.2008410.1002/jcc.20084Suche in Google Scholar PubMed

[34] Raevsky, O. A., Grigor’ev, V. Y., Kireev, D. B., & Zefirov, N. S. (1992). Complete thermodynamic description of H-bonding in the framework of multiplicative approach. Quantitative Structure-Activity Relationships, 11, 49–63. DOI: 10.1002/qsar.19920110109. http://dx.doi.org/10.1002/qsar.1992011010910.1002/qsar.19920110109Suche in Google Scholar

[35] RCSB Protein Data Bank (2009, February). Human estrogen receptor ligand-binding domain in complex with 17beta-estradiol. Retrieved November 1, 2010 from http://www.pdb.org/pdb/explore/explore.do?structureId=1ERE Suche in Google Scholar

[36] Rekker, R. F. (1977). The hydrophobic fragmental constant. Amsterdam, The Netherlands: Elsevier. Suche in Google Scholar

[37] Sadler, B. R., Cho, S. J., Ishaq, K. S., Chae, K., & Korach, K. S. (1998). Three-dimensional quantitative structure-activity relationship study of nonsteroidal estrogen receptor ligands using the comparative molecular field analysis/cross-validated r 2-guided region selection approach. Journal of Medicinal Chemistry, 41, 2261–2267. DOI: 10.1021/jm9705521. http://dx.doi.org/10.1021/jm970552110.1021/jm9705521Suche in Google Scholar PubMed

[38] Schrödinger, LLC. (2010). Jaguar, Version 7.7. New York, NY, USA: Schrödinger, LLC. Suche in Google Scholar

[39] Schultz-Gasch, T., & Stahl, M. (2004). Scoring functions for protein-ligand interactions: a critical perspective. Drug Discovery Today: Technologies, 1, 231–239. DOI: 10.1016/j.ddtec.2004.08.004. http://dx.doi.org/10.1016/j.ddtec.2004.08.00410.1016/j.ddtec.2004.08.004Suche in Google Scholar PubMed

[40] Sgarabotto, P., Ugozzoli, F., Greci, L., Stipa, P., & Carloni, P. (1989). X-ray study of 3-tert-butyl-1-methyl-2-phenylindole, the product of an unexpected tert-butylation reaction. Acta Crystallographica, C45, 1939–1941. DOI: 10.1107/S0108270189004610. 10.1107/S0108270189004610Suche in Google Scholar

[41] Simon, Z., Chiriac, A., Holban, S., Ciubotaru, D., & Mihalas, G. I. (1984). Minimum steric difference. The MTD method for QSAR Studies. Letchworth, UK: Research Studies Press Ltd. Suche in Google Scholar

[42] Sippl, W. (2002a). Development of biologically active compounds by combining 3D QSAR and structure-based design methods. Journal of Computer-Aided Molecular Design, 16, 825–830. DOI: 10.1023/A:1023888813526. http://dx.doi.org/10.1023/A:102388881352610.1023/A:1023888813526Suche in Google Scholar

[43] Sippl, W. (2002b). Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods. Bioorganic & Medicinal Chemistry, 10, 3741–3755. DOI: 10.1016/S0968-0896(02)00375-9. http://dx.doi.org/10.1016/S0968-0896(02)00375-910.1016/S0968-0896(02)00375-9Suche in Google Scholar

[44] Sippl, W. (2000). Receptor-based 3D QSAR analysis of estrogen receptor ligands — merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods. Journal of Computer-Aided Molecular Design, 14, 559–572. DOI: 10.1023/A:1008115913787. http://dx.doi.org/10.1023/A:100811591378710.1023/A:1008115913787Suche in Google Scholar

[45] Sippl, W., & Höltje, H.-D. (2000). Structure-based 3D-QSAR-merging the accuracy of structure-based alignments with the computational efficiency of ligand-based methods. Journal of Molecular Structure: THEOCHEM, 503, 31–50. DOI: 10.1016/S0166-1280(99)00361-9. http://dx.doi.org/10.1016/S0166-1280(99)00361-910.1016/S0166-1280(99)00361-9Suche in Google Scholar

[46] Sun, L., Zhou, Y., Genrong, L., & Li, S. Z. (2004). Molecular electronegativity-distance vector (MEDV-4): a twodimensional QSAR method for the estimation and prediction of biological activities of estradiol derivatives. Journal of Molecular Structure: THEOCHEM, 679, 107–113. DOI: 10.1016/j.theochem.2004.04.010. http://dx.doi.org/10.1016/j.theochem.2004.04.01010.1016/j.theochem.2004.04.010Suche in Google Scholar

[47] Swiss Institute of Bioinformatics (2010, May). NCBI BLAST2 service. Retrieved November 1, 2010, from http://au.expasy.org/tools/blast/ Suche in Google Scholar

[48] Teramoto, R., & Fukunishi, H. (2007). Supervised scoring models with docked ligand conformations for structure-based virtual screening. Journal of Chemical Information and Modeling, 47, 1858–1867. DOI: 10.1021/ci700116z. http://dx.doi.org/10.1021/ci700116z10.1021/ci700116zSuche in Google Scholar PubMed

[49] The Scripps Research Institute (2010, April). MGLTools: AutoDockTools. Retrieved November 1, 2010, from http: //mgltools.scripps.edu/ Suche in Google Scholar

[50] Tirado-Rives, J., & Jorgensen, W. L. (2006). Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. Journal of Medicinal Chemistry, 49, 5880–5884. DOI: 10.1021/jm060763i. http://dx.doi.org/10.1021/jm060763i10.1021/jm060763iSuche in Google Scholar PubMed

[51] Tong, W., Perkins, R., Xing, L., Welsh, W. J., & Sheehan, D. M. (1997). QSAR models for binding of estrogenic compounds to estrogen receptor α and β subtypes. Endocrinology, 138, 4022–4025. DOI: 10.1210/en.138.9.4022. http://dx.doi.org/10.1210/en.138.9.402210.1210/endo.138.9.5487Suche in Google Scholar PubMed

[52] Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. Journal of Computational Chemistry, 31, 455–461. DOI: 10.1002/jcc.21334. 10.1002/jcc.21334Suche in Google Scholar PubMed PubMed Central

[53] Umetrics AB (2001). SIMCA P, Version 9.0 [computer software]. Umeå, Sweden: Umetrics AB. Suche in Google Scholar

[54] Vol’kenshtein, M. V. (1955). Stroenie i fizicheskie svoistva molekul (pp. 230–296). Moscow, USSR: Izdatelstvo Akademii Nauk SSSR. Suche in Google Scholar

[55] von Angerer, E., Prekajac, J., & Strohmeier, J. (1984). 2-Phenylindoles. Relationship between structure, estrogen re ceptor affinity, and mammary tumor inhibiting activity in the rat. Journal of Medicinal Chemistry, 27, 1439–1447. DOI: 10.1021/jm00377a011. http://dx.doi.org/10.1021/jm00377a01110.1021/jm00377a011Suche in Google Scholar PubMed

[56] Waller, C. L. (2004). A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. Journal of Chemical Information and Computer Sciences, 44, 758–765. DOI: 10.1021/ci0342526. 10.1021/ci0342526Suche in Google Scholar PubMed

[57] Wärnmark, A., Treuter, E., Gustafsson, J.-Å., Hubbard, R. E., Brzozowski, A. M., & Pike, A. C. W. (2002). Interaction of transcriptional intermediary factor 2 nuclear receptor box peptides with the coactivator binding site of estrogen receptor α. The Journal of Biological Chemistry, 277, 21862–21868. DOI: 10.1074/jbc.M200764200. http://dx.doi.org/10.1074/jbc.M20076420010.1074/jbc.M200764200Suche in Google Scholar PubMed

[58] Wold, S., Albano, C., Dunn, W. J., Edlund, U., Esbensen, K., Geladi, P., Hellberg, S., Johansson, E., Lindberg, W., & Sjöström, M. (1984). Multivariate data analysis in chemistry. In B. R. Kowalski (Ed.), Chemometrics, mathematics and statistics in chemistry (pp. 17–96). Dordrecht, The Netherlands: D. Reidel Publishing. Suche in Google Scholar

[59] Wolohan, P., & Reichert, D. E. (2003). CoMFA and docking study of novel estrogen receptor subtype selective ligands. Journal of Computer-Aided Molecular Design, 17, 313–328. DOI: 10.1023/A:1026104924132. http://dx.doi.org/10.1023/A:102610492413210.1023/A:1026104924132Suche in Google Scholar

[60] Yu, S. J., Keenan, S. M., Tong, W., & Welsh, W. J. (2002). Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: Predicting the estrogenic activity of xenoestrogens. Chemical Research in Toxicology, 15, 1229–1234. DOI: 10.1021/tx0255875. http://dx.doi.org/10.1021/tx025587510.1021/tx0255875Suche in Google Scholar PubMed

Published Online: 2011-5-21
Published in Print: 2011-8-1

© 2011 Institute of Chemistry, Slovak Academy of Sciences

Artikel in diesem Heft

  1. Lipid retention of novel pressurized extraction vessels as a function of the number of static and flushing cycles, flush volume, and flow rate
  2. Determination of curcuminoids in substances and dosage forms by cyclodextrin-mediated capillary electrophoresis with diode array detection
  3. Interaction of Moringa oleifera seed lectin with humic acid
  4. Hybrid process scheme for the synthesis of ethyl lactate: conceptual design and analysis
  5. Zinc catalyst recycling in the preparation of (all-rac)-α-tocopherol from trimethylhydroquinone and isophytol
  6. Denitrification of simulated nitrate-rich wastewater using sulfamic acid and zinc scrap
  7. Anaerobic treatment of biodiesel by-products in a pilot scale reactor
  8. Preparation of magnesium hydroxide from nitrate aqueous solution
  9. Impact of the type of anodic film formed and deposition time on the characteristics of porous anodic aluminium oxide films containing Ni metal
  10. Synthesis and crystal and molecular structures of N,N′-methylenedipyridinium tetrachlorozincate(II) and N,N′-methylenedipyridinium tetrachlorocadmate(II)
  11. Effects of denaturing acid on the self-association behaviour of poly(ethylene glycol)-block-poly(γ-benzyl l-glutamate)-graft-poly(ethylene glycol) copolymer in ethanol
  12. Properties of poly(γ-benzyl l-glutamate) membrane modified by polyurethane containing carboxyl group
  13. Theoretical thermo-optical patterns relevant to glass crystallisation
  14. Morphology dependence of 1,2-diphenylethylenediamine-derived organogelator templates in solvents and their influence in the production of nanostructured silica
  15. Ferric hydrogensulphate as a recyclable catalyst for the synthesis of fluorescein derivatives
  16. An alternative synthetic process of p-acetaminobenzenesulfonyl chloride through combined chlorosulfonation by HClSO3 and PCl5
  17. An efficient and novel one-pot synthesis of 2,4,5-triaryl-1H-imidazoles catalyzed by UO2(NO3)2·6H2O under heterogeneous conditions
  18. Stereoselective synthesis of the polar part of mycestericins E and G
  19. A regio- and stereoselective three-component synthesis of 5-(trifluoromethyl)-4,5,6,7-tetrahydro-[1,2,4]triazolo[1,5-a]pyrimidine derivatives under solvent-free conditions
  20. Precautions in using global kinetic and thermodynamic models for characterization of drug release from multivalent supports
  21. A sandwich anion receptor by a BODIPY dye bearing two calix[4]pyrrole units
  22. What causes iron-sulphur bonds in active sites of one-iron superoxide reductase and two-iron superoxide reductase to differ?
  23. MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity
Heruntergeladen am 27.11.2025 von https://www.degruyterbrill.com/document/doi/10.2478/s11696-011-0040-3/pdf?lang=de
Button zum nach oben scrollen