Molecular basis of agonism and antagonism in the oestrogen receptor
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DOI: 10.1038/39645
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Cited by:
- Michele Samorani & Manuel Laguna & Robert Kirk DeLisle & Daniel C. Weaver, 2011. "A Randomized Exhaustive Propositionalization Approach for Molecule Classification," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 331-345, August.
- Xueyan Chen & Ugur Uzuner & Man Li & Weibing Shi & Joshua S. Yuan & Susie Y. Dai, 2016. "Phytoestrogens and Mycoestrogens Induce Signature Structure Dynamics Changes on Estrogen Receptor α," IJERPH, MDPI, vol. 13(9), pages 1-14, August.
- Wei He & Wenhui Zhang & Zhenhua Chu & Yu Li, 2021. "Mitigating the Adverse Effects of Polychlorinated Biphenyl Derivatives on Estrogenic Activity via Molecular Modification Techniques," IJERPH, MDPI, vol. 18(9), pages 1-19, May.
- Henrieta Hlisníková & Ida Petrovičová & Branislav Kolena & Miroslava Šidlovská & Alexander Sirotkin, 2020. "Effects and Mechanisms of Phthalates’ Action on Reproductive Processes and Reproductive Health: A Literature Review," IJERPH, MDPI, vol. 17(18), pages 1-37, September.
- Benjamin M. Steiner & Abigail M. Benvie & Derek Lee & Yuwei Jiang & Daniel C. Berry, 2024. "Cxcr4 regulates a pool of adipocyte progenitors and contributes to adiposity in a sex-dependent manner," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
- Peixuan Sun & Wenjin Zhao, 2021. "Strategies to Control Human Health Risks Arising from Antibiotics in the Environment: Molecular Modification of QNs for Enhanced Plant–Microbial Synergistic Degradation," IJERPH, MDPI, vol. 18(20), pages 1-26, October.
- Haigang Zhang & Chengji Zhao & Hui Na, 2020. "Enhanced Biodegradation of Phthalic Acid Esters’ Derivatives by Plasticizer-Degrading Bacteria ( Burkholderia cepacia , Archaeoglobus fulgidus , Pseudomonas aeruginosa ) Using a Correction 3D-QSAR Mod," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
- Fenglei Li & Qiaoyu Hu & Xianglei Zhang & Renhong Sun & Zhuanghua Liu & Sanan Wu & Siyuan Tian & Xinyue Ma & Zhizhuo Dai & Xiaobao Yang & Shenghua Gao & Fang Bai, 2022. "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
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