Using Three-Dimensional Quantitative Structure-Activity Relationships to Examine Estrogen Receptor Binding Affinities of Polychlorinated Hydroxybiphenyls
Author:
Publisher
JSTOR
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health
Cited by 88 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Bone tissue morphology of rat offspring lactationally exposed to polychlorinated biphenyl 169 and 155;Scientific Reports;2020-11-04
2. Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction;Environmental Science & Technology;2020-08-28
3. Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity;Bioinformatics;2019-07-26
4. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction;Molecular Pharmaceutics;2018-08-16
5. Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study;RSC Advances;2018
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