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Chemical space, diversity and activity landscape analysis of estrogen receptor binders
Department of Pharmacy, Universidad Nacional Autónoma de México, Mexico City, Mexico; PECEM, Universidad Nacional Autónoma de México, Mexico City, Mexico; Department of Life Science Informatics, University of Bonn, Bonn, Germany.
Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.ORCID iD: 0000-0003-3107-331X
Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Unit of Work Environment Toxicology, Karolinska Institute, Stockholm, Sweden.
Department of Pharmacy, Universidad Nacional Autónoma de México, Mexico City, Mexico; Medicinal Chemistry Laboratory, University of Veracruz, Veracruz, Mexico.
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2018 (English)In: RSC Advances, E-ISSN 2046-2069, Vol. 8, no 67, p. 38229-38237Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Royal Society of Chemistry, 2018. Vol. 8, no 67, p. 38229-38237
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:oru:diva-83042DOI: 10.1039/c8ra07604aISI: 000451075500013Scopus ID: 2-s2.0-85057215447OAI: oai:DiVA.org:oru-83042DiVA, id: diva2:1439308
Funder
Knut and Alice Wallenberg Foundation, 2013.0253Swedish Research Council, 2016-02031
Note

Ytterligare forskningsfinansiärer:

Consejo Nacional de Ciencia y Tecnologia (CONACyT), Grant Number: 622969

DAAD programme, Grant Number: 57378443

School of Chemistry of the Universidad Nacional Autonoma de Mexico (UNAM), Grant Number: PAIP 5000-9163

Programa de Apoyo a Proyectos para la Innovacion y Mejoramiento de la Ensenanza (PAPIME), Grant Number: PE200118

Universidad Nacional Autonoma de Mexico

Available from: 2018-12-12 Created: 2020-06-12 Last updated: 2022-09-15Bibliographically approved

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Norinder, Ulf

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