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Chemical space, diversity and activity landscape analysis of estrogen receptor binders

机译：雌激素受体结合剂的化学空间，多样性和活性态势分析

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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.
机译：了解内分泌干扰化学物质的结构-活性关系（SAR）在毒理学中具有重要意义。尽管在过去的20年中为雌激素开发了分类器和预测模型，但据我们所知，尚无关于其活动状况或活动悬崖鉴定的研究。在这里，我们使用活动景观模型报告了121种化学物质与雌激素受体结合亲和力的公开数据集的第一个SAR。为此，我们对121种化学物质的化学空间进行了系统的定量和视觉分析。数据集的全局多样性通过最新开发的共识多样性图来表征。将成对的活性差异信息添加到化学空间会引起数据集的活动格局，从而发现异构SAR，特别是对于某些结构类别而言。鉴定出至少八种化合物具有形成活动悬崖的倾向。这项工作的发现进一步扩展了目前对雌激素化合物潜在的SAR的了解，并且可以成为开发新颖且可能会改进的预测模型的起点。

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