亲缘关系
结合亲和力
仿形(计算机编程)
工具箱
雌激素受体
计算生物学
范畴变量
背景(考古学)
药物发现
人类健康
化学
生物
生化工程
计算机科学
生物信息学
受体
机器学习
生物化学
遗传学
乳腺癌
工程类
古生物学
癌症
操作系统
程序设计语言
环境卫生
医学
标识
DOI:10.1080/1062936x.2011.623325
摘要
The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure–activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.
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