The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Ligand-Based Identification of Environmental Estrogens
Abstract
Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm, was used to examine the estrogen receptor (ER) binding affinities of a series of structurally diverse natural, synthetic, and environmental chemicals of interest. The CoMFA/3D-QSAR model is statistically robust and internally consistent, and successfully illustrates that the overall steric and electrostatic properties of structurally diverse ligands for the estrogen receptor are both necessary and sufficient to describe the binding affinity. The ability of the model to accurately predict the ER binding affinity of an external test set of molecules suggests that structure-based 3D-QSAR models may be used to supplement the process of endocrine disruptor identification through prioritization of novel compounds for bioassay. The general application of this 3D-QSAR model within a toxicological framework is, at present, limited only by the quantity and quality of biological data for relevant biomarkers of toxicity and hormonal responsiveness.
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Cites
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
Structure and Function of the Estrogen Receptor
PARKER, M. G., ARBUCKLE, N., DAUVOIS, S., DANIELIAN, P., & WHITE, R. (1993) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1993.tb32276.x · Cited by 129
Cited by
Blair, R. M. (2000) · Toxicological Sciences · doi:10.1093/toxsci/54.1.138 · Cited by 806
Some Alkyl Hydroxy Benzoate Preservatives (Parabens) Are Estrogenic
Routledge, E. J., Parker, J., Odum, J., Ashby, J., & Sumpter, J. P. (1998) · Toxicology and Applied Pharmacology · doi:10.1006/taap.1998.8544 · Cited by 721
Odum, J., Lefevre, P. A., Tittensor, S., Paton, D., Routledge, E. J., Beresford, N. A., Sumpter, J. P., & Ashby, J. (1997) · Regulatory Toxicology and Pharmacology · doi:10.1006/rtph.1997.1100 · Cited by 278
Progress in predicting human ADME parameters in silico
Ekins, S., Waller, C. L., Swaan, P. W., Cruciani, G., Wrighton, S. A., & Wikel, J. H. (2000) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/s1056-8719(00)00109-x · Cited by 274
Self-citationComparative QSAR Analysis of Estrogen Receptor Ligands
Gao, H., Katzenellenbogen, J. A., Garg, R., & Hansch, C. (1999) · Chemical Reviews · doi:10.1021/cr980018g · Cited by 260
Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00546 · Cited by 152
A critical assessment of some biomarker approaches linked with dietary intake
Crews, H., Alink, G., Andersen, R., Braesco, V., Holst, B., Maiani, G., Ovesen, L., Scotter, M., Solfrizzo, M., van den Berg, R., et al. (2001) · British Journal Of Nutrition · doi:10.1079/bjn2001337 · Cited by 99
Influence of endocrine active compounds on the developing rodent brain
Patisaul, H. B., & Polston, E. K. (2008) · Brain Research Reviews · doi:10.1016/j.brainresrev.2007.06.008 · Cited by 81
Metadata
- Authors
- Waller, C. L., Oprea, T. I., Chae, K., Park, H.-K., Korach, K. S., Laws, S. C., Wiese, T. E., Kelce, W. R., & Gray, L. E.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 1996-01-01
- Volume
- 9
- Issue
- 8
- Pages
- 1240-1248
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W2010576107
- Cited by
- 203
- References
- 3
- FWCI
- 19.58
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
