메뉴 건너뛰기




Volumn 4, Issue MAR, 2016, Pages

Predictive modeling of estrogen receptor binding agents using advanced cheminformatics tools and massive public data

Author keywords

Bioassay profiling; Biosimilarity; Endocrine disrupting chemicals; Estrogen receptor ; QSAR modeling

Indexed keywords


EID: 85062093670     PISSN: None     EISSN: 2296665X     Source Type: Journal    
DOI: 10.3389/fenvs.2016.00012     Document Type: Article
Times cited : (34)

References (46)
  • 1
    • 0034020453 scopus 로고    scopus 로고
    • The estrogen receptor relative binding affinities of 188 natural and xenochemicals: structural diversity of ligands
    • Blair, R. M., Fang, H., Branham, W. S., Hass, B. S., Dial, S. L., Moland, C. L., et al. (2000). The estrogen receptor relative binding affinities of 188 natural and xenochemicals: structural diversity of ligands. Toxicol. Sci. 54, 138-153. doi: 10.1093/toxsci/54.1.138
    • (2000) Toxicol. Sci. , vol.54 , pp. 138-153
    • Blair, R.M.1    Fang, H.2    Branham, W.S.3    Hass, B.S.4    Dial, S.L.5    Moland, C.L.6
  • 2
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324
    • (2001) Mach. Learn. , vol.45 , pp. 5-32
    • Breiman, L.1
  • 3
    • 84906303709 scopus 로고    scopus 로고
    • Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: The National Academies Press. Cruz-Monteagudo, M., Medina-Franco, J., Pérez-Castillo, Y., Nicolotti, O., Cordeiro, M. N., and Borges, F. (2014). Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?
    • Committee on Toxicity Testing Assessment of Environmental Agents N.R.C. (2007). Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: The National Academies Press. Cruz-Monteagudo, M., Medina-Franco, J., Pérez-Castillo, Y., Nicolotti, O., Cordeiro, M. N., and Borges, F. (2014). Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discov. Today 19, 1069-1080. doi: 10.1016/j.drudis.2014.02.003
    • (2007) Drug Discov. Today , vol.19 , pp. 1069-1080
  • 5
    • 84893798714 scopus 로고    scopus 로고
    • Prediction of the Estrogen Receptor Binding Affinity for both hER(alpha) and hER(beta) by QSAR Approaches
    • Deng, C. L., Chen, X. X., Lu, H. Y., Yang, X., Luan, F., and Cordeiro, M. (2014). Prediction of the Estrogen Receptor Binding Affinity for both hER(alpha) and hER(beta) by QSAR Approaches. Lett. Drug Des. Disc. 11, 265-278. doi: 10.2174/15701808113109990067
    • (2014) Lett. Drug Des. Disc. , vol.11 , pp. 265-278
    • Deng, C.L.1    Chen, X.X.2    Lu, H.Y.3    Yang, X.4    Luan, F.5    Cordeiro, M.6
  • 6
    • 77957691065 scopus 로고    scopus 로고
    • The EDKB: an established knowledge base for endocrine disrupting chemicals.
    • Ding, D., Xu, L., Fang, H., Hong, H., Perkins, R., Harris, S., et al. (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
    • (2010) BMC Bioinformatics , vol.11
    • Ding, D.1    Xu, L.2    Fang, H.3    Hong, H.4    Perkins, R.5    Harris, S.6
  • 7
    • 0042355453 scopus 로고    scopus 로고
    • Rational selection of training and test sets for the development of validated QSAR models
    • Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y. D., Lee, K. H., and Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aided Mol. Des. 17, 241-253. doi: 10.1023/A:1025386326946
    • (2003) J. Comput. Aided Mol. Des. , vol.17 , pp. 241-253
    • Golbraikh, A.1    Shen, M.2    Xiao, Z.3    Xiao, Y.D.4    Lee, K.H.5    Tropsha, A.6
  • 8
    • 0035813112 scopus 로고    scopus 로고
    • The multifaceted mechanisms of estradiol and estrogen receptor signaling
    • Hall, J. M., Couse, J. F., and Korach, K. S. (2001). The multifaceted mechanisms of estradiol and estrogen receptor signaling. J. Biol. Chem. 276, 36869-36872. doi: 10.1074/jbc.r100029200
    • (2001) J. Biol. Chem. , vol.276 , pp. 36869-36872
    • Hall, J.M.1    Couse, J.F.2    Korach, K.S.3
  • 9
    • 0036156262 scopus 로고    scopus 로고
    • Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts
    • Hong, H., Tong, W., Fang, H., Shi, L., Xie, Q., Wu, J., et al. (2002). Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. Environ. Health Perspect. 110, 29-36. doi: 10.1289/ehp.0211029
    • (2002) Environ. Health Perspect. , vol.110 , pp. 29-36
    • Hong, H.1    Tong, W.2    Fang, H.3    Shi, L.4    Xie, Q.5    Wu, J.6
  • 10
    • 39449135396 scopus 로고    scopus 로고
    • The trouble with QSAR (or how I learned to stop worrying and embrace fallacy)
    • Johnson, S. R. (2008). The trouble with QSAR (or how I learned to stop worrying and embrace fallacy). J. Chem. Inf. Model. 48, 25-26. doi: 10.1021/ci700332k
    • (2008) J. Chem. Inf. Model. , vol.48 , pp. 25-26
    • Johnson, S.R.1
  • 11
    • 84964810430 scopus 로고    scopus 로고
    • Mechanism profiling of hepatotoxicity caused by oxidative stress using the antioxidant response element reporter gene assay models and big data.
    • Kim, M., Huang, R., Sedykh, A., Zhang, J., Xia, M., and Zhu, H. (2016). Mechanism profiling of hepatotoxicity caused by oxidative stress using the antioxidant response element reporter gene assay models and big data. Environ. Health Perspect. doi: 10.1289/ehp.1509763.
    • (2016) Environ. Health Perspect.
    • Kim, M.1    Huang, R.2    Sedykh, A.3    Zhang, J.4    Xia, M.5    Zhu, H.6
  • 12
    • 84897106909 scopus 로고    scopus 로고
    • Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches
    • Kim, M. T., Sedykh, A., Chakravarti, S. K., Saiakhov, R. D., and Zhu, H. (2014). Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharm. Res. 31, 1002-1014. doi: 10.1007/s11095-013-1222-1
    • (2014) Pharm. Res. , vol.31 , pp. 1002-1014
    • Kim, M.T.1    Sedykh, A.2    Chakravarti, S.K.3    Saiakhov, R.D.4    Zhu, H.5
  • 13
    • 78650175254 scopus 로고    scopus 로고
    • The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders
    • Li, J., and Gramatica, P. (2010). The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders. Mol. Divers. 14, 687-696. doi: 10.1007/s11030-009-9212-2
    • (2010) Mol. Divers. , vol.14 , pp. 687-696
    • Li, J.1    Gramatica, P.2
  • 14
    • 38649111953 scopus 로고    scopus 로고
    • Evaluation and QSAR modeling on multiple endpoints of estrogen activity based on different bioassays
    • Liu, H., Papa, E., and 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
    • (2008) Chemosphere , vol.70 , pp. 1889-1897
    • Liu, H.1    Papa, E.2    Gramatica, P.3
  • 15
    • 80052811674 scopus 로고    scopus 로고
    • Review of QSAR Models and Software Tools for Predicting Developmental and Reproductive Toxicity.
    • Luxemborg: Publications Office of the European Union.
    • Lo Piparo, E., and Worth, A. (2010). Review of QSAR Models and Software Tools for Predicting Developmental and Reproductive Toxicity. Luxemborg: Publications Office of the European Union. doi: 10.2788/9628
    • (2010)
    • Lo Piparo, E.1    Worth, A.2
  • 16
    • 84882644463 scopus 로고    scopus 로고
    • Integrative chemical-biological read-across approach for chemical hazard classification
    • Low, Y., Sedykh, A., Fourches, D., Golbraikh, A., Whelan, M., Rusyn, I., et al. (2013). Integrative chemical-biological read-across approach for chemical hazard classification. Chem. Res. Toxicol. 26, 1199-1208. doi: 10.1021/tx400110f
    • (2013) Chem. Res. Toxicol. , vol.26 , pp. 1199-1208
    • Low, Y.1    Sedykh, A.2    Fourches, D.3    Golbraikh, A.4    Whelan, M.5    Rusyn, I.6
  • 17
    • 33746931581 scopus 로고    scopus 로고
    • On outliers and activity cliffs-why QSAR often disappoints
    • Maggiora, G. M. (2006). On outliers and activity cliffs-why QSAR often disappoints. J. Chem. Inf. Model. 46, 1535-1535. doi: 10.1021/ci060117s
    • (2006) J. Chem. Inf. Model. , vol.46 , pp. 1535-1535
    • Maggiora, G.M.1
  • 18
    • 2642544592 scopus 로고    scopus 로고
    • Estrogen receptor-alpha directs ordered, cyclical, and combinatorial recruitment of cofactors on a natural target promoter
    • Métivier, R., Penot, G., Hübner, M. R., Reid, G., Brand, H., Kos, M., et al. (2003). Estrogen receptor-alpha directs ordered, cyclical, and combinatorial recruitment of cofactors on a natural target promoter. Cell 115, 751-763. doi: 10.1016/S0092-8674(03)00934-6
    • (2003) Cell , vol.115 , pp. 751-763
    • Métivier, R.1    Penot, G.2    Hübner, M.R.3    Reid, G.4    Brand, H.5    Kos, M.6
  • 19
    • 84904993806 scopus 로고    scopus 로고
    • Machine learning methods in chemoinformatics
    • Mitchell, J. B. O. (2014). Machine learning methods in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4, 468-481. doi: 10.1002/wcms.1183
    • (2014) Wiley Interdiscip. Rev. Comput. Mol. Sci. , vol.4 , pp. 468-481
    • Mitchell, J.B.O.1
  • 20
    • 0035654437 scopus 로고    scopus 로고
    • Estrogen receptors and endocrine diseases: lessons from estrogen receptor knockout mice
    • Mueller, S. O., and Korach, K. S. (2001). Estrogen receptors and endocrine diseases: lessons from estrogen receptor knockout mice. Curr. Opin. Pharmacol. 1, 613-619. doi: 10.1016/S1471-4892(01)00105-9
    • (2001) Curr. Opin. Pharmacol. , vol.1 , pp. 613-619
    • Mueller, S.O.1    Korach, K.S.2
  • 21
    • 85063845705 scopus 로고    scopus 로고
    • Accessed September 15 2015
    • National Center for Biotechnology Information (2015). PubChem BioAssay Database; AID=743077. (Accessed September 15, 2015).
    • (2015) PubChem BioAssay Database; AID=743077.
  • 22
    • 84951282981 scopus 로고    scopus 로고
    • Development and validation of decision forest model for estrogen receptor binding prediction of chemicals using large data sets.
    • Ng, H. W., Luo, H., Ye, H., Ge, W., Tong, W., Hong, H., et al. (2015). Development and validation of decision forest model for estrogen receptor binding prediction of chemicals using large data sets. Chem. Res. Toxicol. 28, 2343-2351. doi: 10.1021/acs.chemrestox.5b00358
    • (2015) Chem. Res. Toxicol. , vol.28 , pp. 2343-2351
    • Ng, H.W.1    Luo, H.2    Ye, H.3    Ge, W.4    Tong, W.5    Hong, H.6
  • 23
    • 84926370431 scopus 로고    scopus 로고
    • Read-across approaches-Misconceptions, promises and challenges ahead
    • Patlewicz, G., Ball, N., Becker, R. A., Booth, E. D., Cronin, M. T. D., Kroese, D., et al. (2014). Read-across approaches-Misconceptions, promises and challenges ahead. Arch. Med. Vet. 46, 387-396. doi: 10.14573/altex.1410071
    • (2014) Arch. Med. Vet. , vol.46 , pp. 387-396
    • Patlewicz, G.1    Ball, N.2    Becker, R.A.3    Booth, E.D.4    Cronin, M.T.D.5    Kroese, D.6
  • 24
    • 82755163097 scopus 로고    scopus 로고
    • A distinct mechanism for coactivator versus corepressor function by histone methyltransferase G9a in transcriptional regulation
    • Purcell, D. J., Jeong, K. W., Bittencourt, D., Gerke, D. S., and Stallcup, M. R. (2011). A distinct mechanism for coactivator versus corepressor function by histone methyltransferase G9a in transcriptional regulation. J. Biol. Chem. 286, 41963-41971. doi: 10.1074/jbc.m111.298463
    • (2011) J. Biol. Chem. , vol.286 , pp. 41963-41971
    • Purcell, D.J.1    Jeong, K.W.2    Bittencourt, D.3    Gerke, D.S.4    Stallcup, M.R.5
  • 26
    • 70450181710 scopus 로고    scopus 로고
    • How to recognize and workaround pitfalls in QSAR studies: a critical review
    • Scior, T., Medina-Franco, J., Do, Q. T., Martínez-Mayorga, K., Rojas, J., and Bernard, P. (2009). How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr. Med. Chem. 16, 4297-4313. doi: 10.2174/092986709789578213
    • (2009) Curr. Med. Chem. , vol.16 , pp. 4297-4313
    • Scior, T.1    Medina-Franco, J.2    Do, Q.T.3    Martínez-Mayorga, K.4    Rojas, J.5    Bernard, P.6
  • 27
    • 79952352704 scopus 로고    scopus 로고
    • Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity
    • Sedykh, A., Zhu, H., Tang, H., Zhang, L., Richard, A., Rusyn, I., et al. (2011). Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ. Health Perspect. 119, 364-370. doi: 10.1289/ehp.1002476
    • (2011) Environ. Health Perspect. , vol.119 , pp. 364-370
    • Sedykh, A.1    Zhu, H.2    Tang, H.3    Zhang, L.4    Richard, A.5    Rusyn, I.6
  • 29
    • 78651482291 scopus 로고    scopus 로고
    • Endocrine disrupting chemicals targeting estrogen receptor signaling: identification and mechanisms of action
    • Shanle, E. K., and Xu, W. (2011). Endocrine disrupting chemicals targeting estrogen receptor signaling: identification and mechanisms of action. Chem. Res. Toxicol. 24, 6-19. doi: 10.1021/tx100231n
    • (2011) Chem. Res. Toxicol. , vol.24 , pp. 6-19
    • Shanle, E.K.1    Xu, W.2
  • 30
    • 84871210792 scopus 로고    scopus 로고
    • Predicting chemical ocular toxicity using a combinatorial QSAR approach
    • Solimeo, R., Kim, M., Zhu, H., Zhang, J., and Sedykh, A. (2012). Predicting chemical ocular toxicity using a combinatorial QSAR approach. Chem. Res. Toxicol. 25, 2763-2769. doi: 10.1021/tx300393v
    • (2012) Chem. Res. Toxicol. , vol.25 , pp. 2763-2769
    • Solimeo, R.1    Kim, M.2    Zhu, H.3    Zhang, J.4    Sedykh, A.5
  • 31
    • 71849088425 scopus 로고    scopus 로고
    • Pharmacophore and QSAR modeling of estrogen receptor ß ligands and subsequent validation and in silico search for new hits
    • Taha, M. O., Tarairah, M., Zalloum, H., and Abu-Sheikha, G. (2010). Pharmacophore and QSAR modeling of estrogen receptor ß ligands and subsequent validation and in silico search for new hits. J. Mol. Graph. Model. 28, 383-400. doi: 10.1016/j.jmgm.2009.09.005
    • (2010) J. Mol. Graph. Model. , vol.28 , pp. 383-400
    • Taha, M.O.1    Tarairah, M.2    Zalloum, H.3    Abu-Sheikha, G.4
  • 32
    • 36949022890 scopus 로고    scopus 로고
    • Predictive QSAR modeling workflow, model applicability domains, and virtual screening
    • Tropsha, A., and Golbraikh, A. (2007). Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13, 3494-3504. doi: 10.2174/138161207782794257
    • (2007) Curr. Pharm. Des. , vol.13 , pp. 3494-3504
    • Tropsha, A.1    Golbraikh, A.2
  • 33
    • 23844494772 scopus 로고    scopus 로고
    • Cytochrome P450-mediated metabolism of estrogens and its regulation in human
    • Tsuchiya, Y., Nakajima, M., and Yokoi, T. (2005). Cytochrome P450-mediated metabolism of estrogens and its regulation in human. Cancer Lett. 227, 115-124. doi: 10.1016/j.canlet.2004.10.007
    • (2005) Cancer Lett. , vol.227 , pp. 115-124
    • Tsuchiya, Y.1    Nakajima, M.2    Yokoi, T.3
  • 35
    • 84860999989 scopus 로고    scopus 로고
    • VirtualToxLab - A platform for estimating the toxic potential of drugs, chemicals and natural products
    • Vedani, A., Dobler, M., and Smieško, M. (2012). VirtualToxLab - A platform for estimating the toxic potential of drugs, chemicals and natural products. Toxicol. Appl. Pharmacol. 261, 142-153. doi: 10.1016/j.taap.2012.03.018
    • (2012) Toxicol. Appl. Pharmacol. , vol.261 , pp. 142-153
    • Vedani, A.1    Dobler, M.2    Smieško, M.3
  • 36
    • 78449275390 scopus 로고    scopus 로고
    • Chembench: a cheminformatics workbench
    • Walker, T., Grulke, C. M., Tropsha, A., and Pozefsky, D. (2010). Chembench: a cheminformatics workbench. Bioinformatics 26, 3000-3001. doi: 10.1093/bioinformatics/btq556
    • (2010) Bioinformatics , vol.26 , pp. 3000-3001
    • Walker, T.1    Grulke, C.M.2    Tropsha, A.3    Pozefsky, D.4
  • 37
    • 84938739311 scopus 로고    scopus 로고
    • Developing enhanced blood-brain barrier permeability models: integrating external bio-assay data in QSAR modeling
    • Wang, W., Kim, M., Sedykh, A., and Zhu, H. (2015). Developing enhanced blood-brain barrier permeability models: integrating external bio-assay data in QSAR modeling. Pharm. Res. 32, 3055-3065. doi: 10.1007/s11095-015-1687-1
    • (2015) Pharm. Res. , vol.32 , pp. 3055-3065
    • Wang, W.1    Kim, M.2    Sedykh, A.3    Zhu, H.4
  • 38
    • 33751246188 scopus 로고    scopus 로고
    • Similarity-based virtual screening using 2D fingerprints
    • Willett, P. (2006). Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11, 1046-1053. doi: 10.1016/j.drudis.2006.10.005
    • (2006) Drug Discov. Today , vol.11 , pp. 1046-1053
    • Willett, P.1
  • 39
    • 84896504827 scopus 로고    scopus 로고
    • Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods
    • Zang, Q., Rotroff, D. M., and Judson, R. S. (2013). Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. J. Chem. Inf. Model. 53, 3244-3261. doi: 10.1021/ci400527b
    • (2013) J. Chem. Inf. Model. , vol.53 , pp. 3244-3261
    • Zang, Q.1    Rotroff, D.M.2    Judson, R.S.3
  • 40
    • 84903291814 scopus 로고    scopus 로고
    • Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology.
    • Zhang, J., Zhu, H., and Hsieh, J. H. (2014). Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology. PLoS ONE 9:e99863. doi: 10.1371/journal.pone.0099863
    • (2014) PLoS ONE , vol.9
    • Zhang, J.1    Zhu, H.2    Hsieh, J.H.3
  • 41
    • 84882809589 scopus 로고    scopus 로고
    • Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches
    • Zhang, L., Sedykh, A., Tripathi, A., Zhu, H., Afantitis, A., Mouchlis, V. D., et al. (2013). Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicol. Appl. Pharmacol. 272, 67-76. doi: 10.1016/j.taap.2013.04.032
    • (2013) Toxicol. Appl. Pharmacol. , vol.272 , pp. 67-76
    • Zhang, L.1    Sedykh, A.2    Tripathi, A.3    Zhu, H.4    Afantitis, A.5    Mouchlis, V.D.6
  • 42
    • 0000378338 scopus 로고    scopus 로고
    • Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle
    • Zheng, W., and Tropsha, A. (2000). Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inf. Comput. Sci. 40, 185-194. doi: 10.1021/ci980033m
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , pp. 185-194
    • Zheng, W.1    Tropsha, A.2
  • 43
    • 73849128409 scopus 로고    scopus 로고
    • Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure
    • Zhu, H., Martin, T. M., Ye, L., Sedykh, A., Young, D. M., and Tropsha, A. (2009). Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chem. Res. Toxicol. 22, 1913-1921. doi: 10.1021/tx900189p
    • (2009) Chem. Res. Toxicol. , vol.22 , pp. 1913-1921
    • Zhu, H.1    Martin, T.M.2    Ye, L.3    Sedykh, A.4    Young, D.M.5    Tropsha, A.6
  • 44
    • 45749129400 scopus 로고    scopus 로고
    • Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity
    • Zhu, H., Rusyn, I., Richard, A., and Tropsha, A. (2008b). Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environ. Health Perspect. 116, 506-513. doi: 10.1289/ehp.10573
    • (2008) Environ. Health Perspect. , vol.116 , pp. 506-513
    • Zhu, H.1    Rusyn, I.2    Richard, A.3    Tropsha, A.4
  • 45
    • 44449173096 scopus 로고    scopus 로고
    • Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis
    • Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatical, P., et al. (2008a). Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J. Chem. Inf. Model. 48, 766-784. doi: 10.1021/ci700443v
    • (2008) J. Chem. Inf. Model. , vol.48 , pp. 766-784
    • Zhu, H.1    Tropsha, A.2    Fourches, D.3    Varnek, A.4    Papa, E.5    Gramatical, P.6
  • 46
    • 84908137021 scopus 로고    scopus 로고
    • Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants
    • Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., and Moran, K. (2014). Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chem. Res. Toxicol. 27, 1643-1651. doi: 10.1021/tx500145h
    • (2014) Chem. Res. Toxicol. , vol.27 , pp. 1643-1651
    • Zhu, H.1    Zhang, J.2    Kim, M.T.3    Boison, A.4    Sedykh, A.5    Moran, K.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.