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Simplified activity cliff network representations with high interpretability and immediate access to SAR information

Activity cliffs (ACs) consist of structurally similar compounds with a large difference in potency against their target. Accordingly, ACs introduce discontinuity in structure-activity relationships (SARs) and are a prime source of SAR information. In compound data sets, the vast majority of ACs are...

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Autores principales: Hu, Huabin, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367913/
https://www.ncbi.nlm.nih.gov/pubmed/32500478
http://dx.doi.org/10.1007/s10822-020-00319-9
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author Hu, Huabin
Bajorath, Jürgen
author_facet Hu, Huabin
Bajorath, Jürgen
author_sort Hu, Huabin
collection PubMed
description Activity cliffs (ACs) consist of structurally similar compounds with a large difference in potency against their target. Accordingly, ACs introduce discontinuity in structure-activity relationships (SARs) and are a prime source of SAR information. In compound data sets, the vast majority of ACs are formed by differently sized groups of structurally similar compounds with large potency variations. As a consequence, many of these compounds participate in multiple ACs. This coordinated formation of ACs increases their SAR information content compared to ACs considered as individual compound pairs, but complicates AC analysis. In network representations, coordinated ACs give rise to clusters of varying size and topology, which can be interactively and computationally analyzed. While AC networks are indispensable tools to study coordinated ACs, they become difficult to navigate and interpret in the presence of clusters of increasing size and complex topologies. Herein, we introduce reduced network representations that transform AC networks into an easily interpretable format from which SAR information in the form of R-group tables can be readily obtained. The simplified network variant greatly improves the interpretability of large and complex AC networks and substantially supports SAR exploration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10822-020-00319-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-73679132020-07-21 Simplified activity cliff network representations with high interpretability and immediate access to SAR information Hu, Huabin Bajorath, Jürgen J Comput Aided Mol Des Article Activity cliffs (ACs) consist of structurally similar compounds with a large difference in potency against their target. Accordingly, ACs introduce discontinuity in structure-activity relationships (SARs) and are a prime source of SAR information. In compound data sets, the vast majority of ACs are formed by differently sized groups of structurally similar compounds with large potency variations. As a consequence, many of these compounds participate in multiple ACs. This coordinated formation of ACs increases their SAR information content compared to ACs considered as individual compound pairs, but complicates AC analysis. In network representations, coordinated ACs give rise to clusters of varying size and topology, which can be interactively and computationally analyzed. While AC networks are indispensable tools to study coordinated ACs, they become difficult to navigate and interpret in the presence of clusters of increasing size and complex topologies. Herein, we introduce reduced network representations that transform AC networks into an easily interpretable format from which SAR information in the form of R-group tables can be readily obtained. The simplified network variant greatly improves the interpretability of large and complex AC networks and substantially supports SAR exploration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10822-020-00319-9) contains supplementary material, which is available to authorized users. Springer International Publishing 2020-06-05 2020 /pmc/articles/PMC7367913/ /pubmed/32500478 http://dx.doi.org/10.1007/s10822-020-00319-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hu, Huabin
Bajorath, Jürgen
Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title_full Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title_fullStr Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title_full_unstemmed Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title_short Simplified activity cliff network representations with high interpretability and immediate access to SAR information
title_sort simplified activity cliff network representations with high interpretability and immediate access to sar information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367913/
https://www.ncbi.nlm.nih.gov/pubmed/32500478
http://dx.doi.org/10.1007/s10822-020-00319-9
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