Naveja J Jesús, Norinder Ulf, Mucs Daniel, López-López Edgar, Medina-Franco Josė L
Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México Mexico City 04510 Mexico
PECEM, Faculty of Medicine, Universidad Nacional Autónoma de México Mexico City 04510 Mexico.
RSC Adv. 2018 Nov 14;8(67):38229-38237. doi: 10.1039/c8ra07604a.
Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.
了解内分泌干扰化学物质的构效关系（SAR）在毒理学中至关重要。尽管在过去20年中已针对雌激素开发了分类器和预测模型，但据我们所知，尚无关于其活性格局或活性悬崖识别的研究。在此，我们使用活性格局建模报告了121种具有雌激素受体结合亲和力报告的化学物质的公共数据集的首个SAR。为此，我们对这121种化学物质的化学空间进行了系统的定量和可视化分析。通过最近开发的共识多样性图方法对数据集的全局多样性进行了表征。将成对活性差异信息添加到化学空间中，产生了数据集的活性格局，揭示了异质的SAR，特别是对于某些结构类别。至少鉴定出八种具有形成活性悬崖高倾向的化合物。这项工作的发现进一步扩展了当前对雌激素化合物潜在SAR的认识，并且可以作为开发新颖且可能改进的预测模型的起点。
