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Set-Theoretic Formalism for Treating Ligand-Target Datasets

Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity...

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Autores principales: Maggiora, Gerald, Vogt, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704321/
https://www.ncbi.nlm.nih.gov/pubmed/34946500
http://dx.doi.org/10.3390/molecules26247419
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author Maggiora, Gerald
Vogt, Martin
author_facet Maggiora, Gerald
Vogt, Martin
author_sort Maggiora, Gerald
collection PubMed
description Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes.
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spelling pubmed-87043212021-12-25 Set-Theoretic Formalism for Treating Ligand-Target Datasets Maggiora, Gerald Vogt, Martin Molecules Article Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes. MDPI 2021-12-07 /pmc/articles/PMC8704321/ /pubmed/34946500 http://dx.doi.org/10.3390/molecules26247419 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Maggiora, Gerald
Vogt, Martin
Set-Theoretic Formalism for Treating Ligand-Target Datasets
title Set-Theoretic Formalism for Treating Ligand-Target Datasets
title_full Set-Theoretic Formalism for Treating Ligand-Target Datasets
title_fullStr Set-Theoretic Formalism for Treating Ligand-Target Datasets
title_full_unstemmed Set-Theoretic Formalism for Treating Ligand-Target Datasets
title_short Set-Theoretic Formalism for Treating Ligand-Target Datasets
title_sort set-theoretic formalism for treating ligand-target datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704321/
https://www.ncbi.nlm.nih.gov/pubmed/34946500
http://dx.doi.org/10.3390/molecules26247419
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