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Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets

[Image: see text] Activity cliffs are formed by structurally analogous compounds with large potency variations and are highly relevant for the exploration of discontinuous structure–activity relationships and compound optimization. So far, activity cliffs have mostly been studied on a case-by-case b...

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Detalles Bibliográficos
Autores principales: Hu, Huabin, Stumpfe, Dagmar, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644420/
https://www.ncbi.nlm.nih.gov/pubmed/31458921
http://dx.doi.org/10.1021/acsomega.8b01188
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author Hu, Huabin
Stumpfe, Dagmar
Bajorath, Jürgen
author_facet Hu, Huabin
Stumpfe, Dagmar
Bajorath, Jürgen
author_sort Hu, Huabin
collection PubMed
description [Image: see text] Activity cliffs are formed by structurally analogous compounds with large potency variations and are highly relevant for the exploration of discontinuous structure–activity relationships and compound optimization. So far, activity cliffs have mostly been studied on a case-by-case basis or assessed by global statistical analysis. Different from previous investigations, we report a large-scale analysis of activity cliff formation with a strong focus on individual compound activity classes (target sets). Compound potency distributions were systematically analyzed and categorized, and structural relationships were dissected and visualized on a per-set basis. Our study uncovered target set-dependent interplay of potency distributions and structural relationships and revealed the presence of activity cliffs and origins of cliff formation in different structure–activity relationship environments.
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spelling pubmed-66444202019-08-27 Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets Hu, Huabin Stumpfe, Dagmar Bajorath, Jürgen ACS Omega [Image: see text] Activity cliffs are formed by structurally analogous compounds with large potency variations and are highly relevant for the exploration of discontinuous structure–activity relationships and compound optimization. So far, activity cliffs have mostly been studied on a case-by-case basis or assessed by global statistical analysis. Different from previous investigations, we report a large-scale analysis of activity cliff formation with a strong focus on individual compound activity classes (target sets). Compound potency distributions were systematically analyzed and categorized, and structural relationships were dissected and visualized on a per-set basis. Our study uncovered target set-dependent interplay of potency distributions and structural relationships and revealed the presence of activity cliffs and origins of cliff formation in different structure–activity relationship environments. American Chemical Society 2018-07-11 /pmc/articles/PMC6644420/ /pubmed/31458921 http://dx.doi.org/10.1021/acsomega.8b01188 Text en Copyright © 2018 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Hu, Huabin
Stumpfe, Dagmar
Bajorath, Jürgen
Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title_full Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title_fullStr Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title_full_unstemmed Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title_short Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
title_sort rationalizing the formation of activity cliffs in different compound data sets
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644420/
https://www.ncbi.nlm.nih.gov/pubmed/31458921
http://dx.doi.org/10.1021/acsomega.8b01188
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