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QSAR modeling of aromatase inhibition by flavonoids using machine learning approaches

  • Chanin Nantasenamat EMAIL logo , Apilak Worachartcheewan , Prasit Mandi , Teerawat Monnor , Chartchalerm Isarankura-Na-Ayudhya und Virapong Prachayasittikul
Veröffentlicht/Copyright: 28. Januar 2014
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Abstract

Aromatase is a member of the cytochrome P450 family responsible for catalyzing the rate-limiting conversion of androgens to estrogens. In the pursuit of robust aromatase inhibitors, quantitative structure-activity relationship (QSAR) and classification structure-activity relationship (CSAR) studies were performed on a non-redundant set of 63 flavonoids using multiple linear regression, artificial neural network, support vector machine and decision tree approaches. Easy-to-interpret descriptors providing comprehensive coverage on general characteristics of molecules (i.e., molecular size, flexibility, polarity, solubility, charge and electronic properties) were employed to describe the unique physicochemical properties of the investigated flavonoids. QSAR models provided good predictive performance as observed from their statistical parameters with Q values in the range of 0.8014 and 0.9870 for the cross-validation set and Q values in the range of 0.8966 and 0.9943 for the external test set. Furthermore, CSAR models developed with the J48 algorithm are able to accurately classify flavonoids as active and inactive as observed from the percentage of correctly classified instances in the range of 84.6 % and 100 %. The study presented herein represents the first large-scale QSAR study of aromatase inhibition on a large set of flavonoids. Such investigations provide an important insight on the origins of aromatase inhibitory properties of flavonoids as breast cancer therapeutics.

[1] Brueggemeier, R. W., Gu, X. J., Mobley, J. A., Joomprabutra, S., Bhat, A. S., & Whetstone, J. L. (2001). Effects of phytoestrogens and synthetic combinatorial libraries on aromatase, estrogen biosynthesis, and metabolism. Annals of the New York Academy of Sciences, 948, 51–66. DOI: 10.1111/j.1749-6632.2001.tb03986.x. http://dx.doi.org/10.1111/j.1749-6632.2001.tb03986.x10.1111/j.1749-6632.2001.tb03986.xSuche in Google Scholar

[2] Brueggemeier, R. W., Hackett, J. C., & Diaz-Cruz, E. S. (2005). Aromatase inhibitors in the treatment of breast cancer. Endocrine Reviews, 26, 331–345. DOI: 10.1210/er.2004-0015. http://dx.doi.org/10.1210/er.2004-001510.1210/er.2004-0015Suche in Google Scholar

[3] Cortes, C., & Vapnik, V. (1995). Support-vector network. Machine Learning, 20, 273–297. DOI: 10.1007/bf00994018. 10.1007/BF00994018Suche in Google Scholar

[4] Dutta, U., & Pant, K. (2008). Aromatase inhibitors: past, present and future in breast cancer therapy. Medical Oncology, 25, 113–124. DOI: 10.1007/s12032-007-9019-x. http://dx.doi.org/10.1007/s12032-007-9019-x10.1007/s12032-007-9019-xSuche in Google Scholar

[5] Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004). Data mining in bioinformatics using Weka. Bioinformatics, 20, 2479–2481. DOI: 10.1093/bioinformatics/bth261. http://dx.doi.org/10.1093/bioinformatics/bth26110.1093/bioinformatics/bth261Suche in Google Scholar

[6] Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G. A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H. P., Izmaylov, A. F., Bloino, J., Zheng, G., Sonnenberg, J. L., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Vreven, T., Montgomery, J. A., Peralta, J. E., Ogliaro, F., Bearpark, M., Heyd, J. J., Brothers, E., Kudin, K. N., Staroverov, V. N., Kobayashi, R., Normand, J., Raghavachari, K., Rendell, A., Burant, J. C., Iyengar, S. S., Tomasi, J., Cossi, M., Rega, N., Millam, J. M., Klene, M., Knox, J. E., Cross, J. B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R. E., Yazyev, O., Austin, A. J., Cammi, R., Pomelli, C., Ochterski, J. W., Martin, R. L., Morokuma, K., Zakrzewski, V. G., Voth, G. A., Salvador, P., Dannenberg, J. J., Dapprich, S., Daniels, A. D., Farkas, Ö., Foresman, J. B., Ortiz, J. V., Cioslowski, J., & Fox, D. J. (2009). Gaussian 09, Revision A.1 [computer software]. Wallingford, Connecticut, USA: Gaussian. Suche in Google Scholar

[7] Gobbi, S., Cavalli, A., Rampa, A., Belluti, F., Piazzi, L., Paluszcak, A., Hartmann, R. W., Recanatini, M., & Bisi, A. (2006). Lead optimization providing a series of flavone derivatives as potent nonsteroidal inhibitors of the cytochrome P450 aromatase enzyme. Journal of Medicinal Chemistry, 49, 4777–4780. DOI: 10.1021/jm060186y. http://dx.doi.org/10.1021/jm060186y10.1021/jm060186ySuche in Google Scholar

[8] Isarankura-Na-Ayudhya, C., Nantasenamat, C., Buraparuangsang, P., Piacham, T., Ye, L., Bülow, L., & Prachayasittikul, V. (2008). Computational insights on sulfonamide imprinted polymers. Molecules, 13, 3077–3091. DOI: 10.3390/molecules13123077. http://dx.doi.org/10.3390/molecules1312307710.3390/molecules13123077Suche in Google Scholar

[9] Jalali-Heravi, M., & Parastar, F. (2000). Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. Journal of Chemical Information and Computer Sciences, 40, 147–154. DOI: 10.1021/ci990314+. 10.1021/ci990314+Suche in Google Scholar

[10] Kao, Y. C., Zhou, C. B., Sherman, M., Laughton, C. A., & Chen, S. (1998). Molecular basis of the inhibition of human aromatase (estrogen synthetase) by flavone and isoflavone phytoestrogens: A site-directed mutagenesis study. Environmental Health Perspectives, 106, 85–92. DOI: 10.1289/ehp.9810 685. http://dx.doi.org/10.1289/ehp.9810685Suche in Google Scholar

[11] Le Bail, J. C., Pouget, C., Fagnere, C., Basly, J. P., Chulia, A. J., & Habrioux, G. (2001). Chalcones are potent inhibitors of aromatase and 17β-hydroxysteroid dehydrogenase activities. Life Sciences, 68, 751–761. DOI: 10.1016/s0024-3205(00)00974-7. http://dx.doi.org/10.1016/S0024-3205(00)00974-710.1016/S0024-3205(00)00974-7Suche in Google Scholar

[12] Liu, M. M., Huang, Y., & Wang, J. (2012). Developing phytoestrogens for breast cancer prevention. Anti-Cancer Agents in Medicinal Chemistry, 12, 1306–1313. DOI: 10.2174/187152012803833062. http://dx.doi.org/10.2174/18715201280383306210.2174/187152012803833062Suche in Google Scholar PubMed

[13] Mandi, P., Nantasenamat, C., Srungboonmee, K., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2012). QSAR study of anti-prion activity of 2-aminothiazoles. EXCLI Journal, 11, 453–467. Suche in Google Scholar

[14] Mohammed, H. A., Ba, L. A., Burkholz, T., Schumann, E., Diesel, B., Zapp, J., Kiemer, A. K., Ries, C., Hartmann, R. W., Hosny, M., & Jacob, C. (2011). Facile synthesis of chrysin-derivatives with promising activities as aromatase inhibitors. Natural Product Communications, 6, 31–34. 10.1177/1934578X1100600108Suche in Google Scholar

[15] Monteiro, R., Becker, H., Azevedo, I., & Calhau, C. (2006). Effect of hop (Humulus lupulus L.) flavonoids on aromatase (estrogen synthase) activity. Journal of Agricultural and Food Chemistry, 54, 2938–2943. DOI: 10.1021/jf053162t. http://dx.doi.org/10.1021/jf053162t10.1021/jf053162tSuche in Google Scholar PubMed

[16] Mullen, L. M. A., Duchowicz, P. R., & Castro, E. A. (2011). QSAR treatment on a new class of triphenylmethylcontaining compounds as potent anticancer agents. Chemo metrics and Intelligent Laboratory Systems, 107, 269–275. DOI: 10.1016/j.chemolab.2011.04.011. http://dx.doi.org/10.1016/j.chemolab.2011.04.01110.1016/j.chemolab.2011.04.011Suche in Google Scholar

[17] Nabholtz, J. M., Mouret-Reynier, M. A., Durando, X., Van Praagh, I., Al-Sukhun, S., Ferriere, J. P., & Chollet, P. (2009). Comparative review of anastrozole, letrozole and exemestane in the management of early breast cancer. Expert Opinion on Pharmacotherapy, 10, 1435–1447. DOI: 10.1517/14656560902953738. http://dx.doi.org/10.1517/1465656090295373810.1517/14656560902953738Suche in Google Scholar PubMed

[18] Nagar, S., Islam, M. A., Das, S., Mukherjee, A., & Saha, A. (2008). Pharmacophore mapping of flavone derivatives for aromatase inhibition. Molecular Diversity, 12, 65–76. DOI: 10.1007/s11030-008-9077-9. http://dx.doi.org/10.1007/s11030-008-9077-910.1007/s11030-008-9077-9Suche in Google Scholar PubMed

[19] Nantasenamat, C., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2005). Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. Journal of Computer-Aided Molecular Design, 19, 509–524. DOI 10.1007/s10822-005-9004-4. http://dx.doi.org/10.1007/s10822-005-9004-410.1007/s10822-005-9004-4Suche in Google Scholar PubMed

[20] Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2007a). Quantitative structureimprinting factor relationship of molecularly imprinted polymers. Biosensors and Bioelectronics, 22, 3309–3317. DOI: 10.1016/j.bios.2007.01.017. http://dx.doi.org/10.1016/j.bios.2007.01.01710.1016/j.bios.2007.01.017Suche in Google Scholar PubMed

[21] Nantasenamat, C., Isarankura-Na-Ayudhya, C., Tansila, N., Naenna, T., & Prachayasittikul, V. (2007b). Prediction of GFP spectral properties using artificial neural network. Journal of Computational Chemistry, 28, 1275–1289. DOI: 10.1002/jcc.20656. http://dx.doi.org/10.1002/jcc.2065610.1002/jcc.20656Suche in Google Scholar PubMed

[22] Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2008). Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. Journal of Molecular Graphics and Modelling, 27, 188–196. DOI: 10.1016/j.jmgm.2008.04.005. http://dx.doi.org/10.1016/j.jmgm.2008.04.00510.1016/j.jmgm.2008.04.005Suche in Google Scholar PubMed

[23] Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2009). A practical overview of quantitative structure-activity relationship. EXCLI Journal, 8, 74–88. Suche in Google Scholar

[24] Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2010). Advances in computational methods to predict the biological activity of compounds. Expert Opinion on Drug Discovery, 5, 633–654. DOI: 10.1517/17460441.2010.492827. http://dx.doi.org/10.1517/17460441.2010.49282710.1517/17460441.2010.492827Suche in Google Scholar PubMed

[25] Nantasenamat, C., Li, H., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2012). Exploring the physicochemical properties of templates from molecular imprinting literature using interactive text mining approach. Chemometrics and Intelligent Laboratory Systems, 116, 128–136. DOI: 10.1016/j.chemolab.2012.05.006. http://dx.doi.org/10.1016/j.chemolab.2012.05.00610.1016/j.chemolab.2012.05.006Suche in Google Scholar

[26] Nantasenamat, C., Srungboonmee, K., Jamsak, S., Tansila, N., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2013a). Quantitative structure-property relationship study of spectral properties of green fluorescent protein with support vector machine. Chemometrics and Intelligent Laboratory Systems, 120, 42–52. DOI: 10.1016/j.chemolab.2012.11.003. http://dx.doi.org/10.1016/j.chemolab.2012.11.00310.1016/j.chemolab.2012.11.003Suche in Google Scholar

[27] Nantasenamat, C., Li, H., Mandi, P., Worachartcheewan, A., Monnor, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2013b). Exploring the chemical space of aromatase inhibitors. Molecular Diversity. DOI: 10.1007/s11030-013-9462-x. 10.1007/s11030-013-9462-xSuche in Google Scholar

[28] Narayana, B. L., Kishore, D. P., Balakumar, C., Rao, K. V., Kaur, R., Rao, A. R., Murthy, J. N., & Ravikumar, M. (2012). Molecular modeling evaluation of non-steroidal aromatase inhibitors. Chemical Biology & Drug Design, 79, 674–682. DOI: 10.1111/j.1747-0285.2011.01277.x. http://dx.doi.org/10.1111/j.1747-0285.2011.01277.x10.1111/j.1747-0285.2011.01277.xSuche in Google Scholar

[29] O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. DOI: 10.1186/1758-2946-3-33. http://dx.doi.org/10.1186/1758-2946-3-3310.1186/1758-2946-3-33Suche in Google Scholar

[30] OpenEye Scientific Software (2013). VIDA, Version 4.2.1 [computer software]. Santa Fe, NM, USA: OpenEye Scientific Software. Suche in Google Scholar

[31] Pelissero, C., Lenczowski, M. J. P., Chinzi, D., Davail-Cuisset, B., Sumpter, J. P., & Fostier, A. (1996). Effects of flavonoids on aromatase activity, an in vitro study. Journal of Steroid Biochemistry and Molecular Biology, 57, 215–223. DOI: 10.1016/0960-0760(95)00261-8. http://dx.doi.org/10.1016/0960-0760(95)00261-810.1016/0960-0760(95)00261-8Suche in Google Scholar

[32] Piacham, T., Isarankura-Na-Ayudhya, C., Nantasenamat, C., Yainoy, S., Ye, L., Bülow, L., & Prachayasittikul, V. (2006). Metalloantibiotic Mn(II)-bacitracin complex mimicking manganese superoxide dismutase. Biochemical and Biophysical Research Communications, 341, 925–930. DOI: 10.1016/j.bbrc.2006.01.045. http://dx.doi.org/10.1016/j.bbrc.2006.01.04510.1016/j.bbrc.2006.01.045Suche in Google Scholar PubMed

[33] Piacham, T., Nantasenamat, C., Suksrichavalit, T., Puttipanyalears, C., Pissawong, T., Maneewas, S., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). Synthesis and theoretical study of molecularly imprinted nanospheres for recognition of tocopherols. Molecules, 14, 2985–3002. DOI: 10.3390/molecules14082985. http://dx.doi.org/10.3390/molecules1408298510.3390/molecules14082985Suche in Google Scholar PubMed PubMed Central

[34] Pingaew, R., Tongraung, P., Worachartcheewan, A., Nantasenamat, C., Prachayasittikul, S., Ruchirawat, S., & Prachayasittikul, V. (2013). Cytotoxicity and QSAR study of (thio)ureas derived from phenylalkylamines and pyridylalkylamines. Medicinal Chemistry Research, 22, 4016–4029. DOI: 10.1007/s00044-012-0402-6. http://dx.doi.org/10.1007/s00044-012-0402-610.1007/s00044-012-0402-6Suche in Google Scholar

[35] Pouget, C., Fagnere, C., Basly, J. P., Besson, A. E., Champavier, Y., Habrioux, G., & Chulia, A. J. (2002a). Synthesis and aromatase inhibitory activity of flavanones. Pharmaceutical Research, 19, 286–291. DOI: 10.1023/a:1014490817731. http://dx.doi.org/10.1023/A:101449081773110.1023/A:1014490817731Suche in Google Scholar

[36] Pouget, C., Fagnere, C., Basly, J. P., Habrioux, G., & Chulia, A. J. (2002b). New aromatase inhibitors. Synthesis and inhibitory activity of pyridinyl-substituted flavanone derivatives. Bioorganic & Medicinal Chemistry Letters, 12, 1059–1061. DOI: 10.1016/s0960-894x(02)00072-0. http://dx.doi.org/10.1016/S0960-894X(02)00072-010.1016/S0960-894X(02)00072-0Suche in Google Scholar

[37] Prachayasittikul, V., Isarankura-Na-Ayudhya, C., Tantimongcolwat, T., Nantasenamat, C., & Galla, H. J. (2007). EDTAinduced membrane fluidization and destabilization: Biophysical studies on artificial lipid membranes. Acta Biochimica et Biophysica Sinica, 39, 901–913. DOI: 10.1111/j.1745-7270.2007.00350.x. http://dx.doi.org/10.1111/j.1745-7270.2007.00350.x10.1111/j.1745-7270.2007.00350.xSuche in Google Scholar PubMed

[38] Prachayasittikul, S., Wongsawatkul, O., Worachartcheewan, A., Nantasenamat, C., Ruchirawat, S., & Prachayasittikul, V. (2010). Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives. Molecules, 15, 198–214. DOI: 10.3390/molecules15010198. http://dx.doi.org/10.3390/molecules1501019810.3390/molecules15010198Suche in Google Scholar PubMed PubMed Central

[39] Sainsbury, R. (2013). The development of endocrine therapy for women with breast cancer. Cancer Treatment Reviews, 39, 507–517. DOI: 10.1016/j.ctrv.2012.07.006. http://dx.doi.org/10.1016/j.ctrv.2012.07.00610.1016/j.ctrv.2012.07.006Suche in Google Scholar PubMed

[40] Simpson, E. R., Mahendroo, M. S., Means, G. D., Kilgore, M. W., Hinshelwood, M. M., Graham-Lorence, S., Amarneh, B., Ito, Y., Fisher, C. R., Michael, M. D., Mendelson, C. R., & Bulun, S. E. (1994). Aromatase cytochrome P450, the enzyme responsible for estrogen biosynthesis. Endocrine Reviews, 15, 342–355. DOI: 10.1210/edrv-15-3-342. 10.1210/edrv-15-3-342Suche in Google Scholar PubMed

[41] Suksrichavalit, T., Prachayasittikul, S., Piacham, T., Isarankura-Na-Ayudhya, C., Nantasenamat, C., & Prachayasittikul, V. (2008). Copper complexes of nicotinic-aromatic carboxylic acids as superoxide dismutase mimetics. Molecules, 13, 3040–3056. DOI: 10.3390/molecules13123040. http://dx.doi.org/10.3390/molecules1312304010.3390/molecules13123040Suche in Google Scholar PubMed PubMed Central

[42] Suksrichavalit, T., Prachayasittikul, S., Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). Copper complexes of pyridine derivatives with superoxide scavenging and antimicrobial activities. European Journal of Medicinal Chemistry, 44, 3259–3265. DOI: 10.1016/j.ejmech.2009.03.033. http://dx.doi.org/10.1016/j.ejmech.2009.03.03310.1016/j.ejmech.2009.03.033Suche in Google Scholar PubMed

[43] Talete (2007). Dragon for windows (software for molecular descriptor calculations), version 5.5 [computer software]. Milano, Italy: Talete. Suche in Google Scholar

[44] Thippakorn, C., Suksrichavalit, T., Nantasenamat, C., Tantimongcolwat, T., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2009). Modeling the LPS neutralization activity of anti-endotoxins. Molecules, 14, 1869–1888. DOI: 10.3390/molecules14051869. http://dx.doi.org/10.3390/molecules1405186910.3390/molecules14051869Suche in Google Scholar PubMed PubMed Central

[45] Todeschini, R., & Consonni, V. (2009). Molecular descriptors for chemoinformatics. Weinheim, Germany: Wiley. http://dx.doi.org/10.1002/978352762876610.1002/9783527628766Suche in Google Scholar

[46] Vapnik, V. (1998). Statistical learning theory. New York, NY, USA: Wiley. Suche in Google Scholar

[47] Wang, Y., Gho, W. M., Chan, F. L., Chen, S., & Leung, L. K. (2008). The red clover (Trifolium pratense) isoflavone biochanin A inhibits aromatase activity and expression. British Journal of Nutrition, 99, 303–310. DOI: 10.1017/s0007114507811974. http://dx.doi.org/10.1017/S000711450781197410.1017/S0007114507811974Suche in Google Scholar PubMed

[48] Whitehead, S. A., & Lacey, M. (2003). Phytoestrogens inhibit aromatase but not 17β-hydroxysteroid dehydrogenase (HSD) type 1 in human granulosa-luteal cells: evidence for FSH induction of 17β-HSD. Human Reproduction, 18, 487–494. DOI: 10.1093/humrep/deg125. http://dx.doi.org/10.1093/humrep/deg12510.1093/humrep/deg125Suche in Google Scholar PubMed

[49] Worachartcheewan, A., Nantasenamat, C., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). Modeling the activity of furin inhibitors using artificial neural network. European Journal of Medicinal Chemistry, 44, 1664–1673. DOI: 10.1016/j.ejmech.2008.09.028. http://dx.doi.org/10.1016/j.ejmech.2008.09.02810.1016/j.ejmech.2008.09.028Suche in Google Scholar PubMed

[50] Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Pidetcha, P., & Prachayasittikul, V. (2010). Identification of metabolic syndrome using decision tree analysis. Diabetes Research and Clinical Practice, 90, e15–e18. DOI: 10.1016/j.diabres.2010.06.009. http://dx.doi.org/10.1016/j.diabres.2010.06.00910.1016/j.diabres.2010.06.009Suche in Google Scholar PubMed

[51] Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, S., & Prachayasittikul, V. (2011). Predicting the free radical scavenging activity of curcumin derivatives. Chemometrics and Intelligent Laboratory Systems, 109, 207–216. DOI: 10.1016/j.chemolab.2011.09.010. http://dx.doi.org/10.1016/j.chemolab.2011.09.01010.1016/j.chemolab.2011.09.010Suche in Google Scholar

[52] Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2013). QSAR study of amidino bis-benzimidazole derivatives as potent antimalarial agents against Plasmodium falciparum. Chemical Papers, 67, 1462–1473. DOI: 10.2478/s11696-013-0398-5. http://dx.doi.org/10.2478/s11696-013-0398-510.2478/s11696-013-0398-5Suche in Google Scholar

[53] Yahiaoui, S., Pouget, C., Fagnere, C., Champavier, Y., Habrioux, G., & Chulia, A. J. (2004). Synthesis and evaluation of 4-triazolylflavans as new aromatase inhibitors. Bioorganic & Medicinal Chemistry Letters, 14, 5215–5218. DOI: 10.1016/j.bmcl.2004.07.090. http://dx.doi.org/10.1016/j.bmcl.2004.07.09010.1016/j.bmcl.2004.07.090Suche in Google Scholar PubMed

[54] Yahiaoui, S., Pouget, C., Buxeraud, J., Chulia, A. J., & Fagnère, C. (2011). Lead optimization of 4-imidazolylflavans: New promising aromatase inhibitors. European Journal of Medicinal Chemistry, 46, 2541–2545. DOI: 10.1016/j.ejmech.2011. 03.043. http://dx.doi.org/10.1016/j.ejmech.2011.03.04310.1016/j.ejmech.2011.03.043Suche in Google Scholar PubMed

[55] Zou, C., & Zhou, L. (2007). QSAR study of oxazolidinone antibacterial agents using artificial neural networks. Molecular Simulation, 33, 517–530. DOI: 10.1080/08927020601188528. http://dx.doi.org/10.1080/0892702060118852810.1080/08927020601188528Suche in Google Scholar

Published Online: 2014-1-28
Published in Print: 2014-5-1

© 2013 Institute of Chemistry, Slovak Academy of Sciences

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Heruntergeladen am 22.9.2025 von https://www.degruyterbrill.com/document/doi/10.2478/s11696-013-0498-2/html
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