Home MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity
Article
Licensed
Unlicensed Requires Authentication

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 and Ludovic Kurunczi
Published/Copyright: May 21, 2011
Become an author with De Gruyter Brill
Author Information
Chemical Papers
From the journal Chemical Papers Volume 65 Issue 4

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.

Keywords: MTD-PLS method; molecular docking; FRED; 2-phenylindole derivatives

[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_5Search 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/s11696-009-0026-6Search 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/BF00126743Search 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)00204-8Search 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/10.1038/3964510.1038/39645Search 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.200390008Search 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)00616-4Search 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-S5Search 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/jm030489hSearch 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/cr980018gSearch 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/ci990437uSearch 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:1025386326946Search 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)00123-1Search 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:1020869118689Search 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)00306-1Search in Google Scholar

[16] Hypercube, Inc. (2005). HyperChem, Version 7.52 for Windows [computer software]. Gainesville, FL, USA: Hypercube Inc. Search 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/s10822-008-9196-5Search 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/pac200375112397Search 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/s00214-006-0138-9Search 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/jm0700262Search 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/ci050077cSearch 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.2006.01.007Search 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.2007.07.071Search 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/ci0501645Search 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. Search 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.0707515Search 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/s11696-008-0089-9Search 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.76Search in Google Scholar PubMed PubMed Central

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

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

[31] OpenEye Scientific Software, Inc. (2008). OMEGA, Version 2.3.2. Santa Fe, NM, USA: OpenEye Scientific Software, Inc. Search 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/10.1080/1062936010803537210.1080/10629360108035372Search 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.20084Search 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.19920110109Search 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 Search in Google Scholar

[36] Rekker, R. F. (1977). The hydrophobic fragmental constant. Amsterdam, The Netherlands: Elsevier. Search 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/jm9705521Search in Google Scholar PubMed

[38] Schrödinger, LLC. (2010). Jaguar, Version 7.7. New York, NY, USA: Schrödinger, LLC. Search 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.2004.08.004Search 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/S0108270189004610Search 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. Search 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:1023888813526Search 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)00375-9Search 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:1008115913787Search 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)00361-9Search 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.2004.04.010Search in Google Scholar

[47] Swiss Institute of Bioinformatics (2010, May). NCBI BLAST2 service. Retrieved November 1, 2010, from http://au.expasy.org/tools/blast/ Search 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/ci700116zSearch in Google Scholar PubMed

[49] The Scripps Research Institute (2010, April). MGLTools: AutoDockTools. Retrieved November 1, 2010, from http: //mgltools.scripps.edu/ Search 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/jm060763iSearch 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.138.9.5487Search 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.21334Search in Google Scholar PubMed PubMed Central

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

[54] Vol’kenshtein, M. V. (1955). Stroenie i fizicheskie svoistva molekul (pp. 230–296). Moscow, USSR: Izdatelstvo Akademii Nauk SSSR. Search 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/jm00377a011Search 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/ci0342526Search 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.M200764200Search 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. Search 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:1026104924132Search 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/tx0255875Search in Google Scholar PubMed

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

© 2011 Institute of Chemistry, Slovak Academy of Sciences

Articles in the same Issue

  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
https://doi.org/10.2478/s11696-011-0040-3
Keywords for this article
MTD-PLS method; molecular docking; FRED; 2-phenylindole derivatives

Articles in the same Issue

  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
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 26.11.2025 from https://www.degruyterbrill.com/document/doi/10.2478/s11696-011-0040-3/pdf
Scroll to top button