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A note on utilising binary features as ligand descriptors

It is common in cheminformatics to represent the properties of a ligand as a string of 1’s and 0’s, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant featur...

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Detalles Bibliográficos
Autores principales: Mussa, Hamse Y., Mitchell, John B. O., Glen, Robert C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4665894/
https://www.ncbi.nlm.nih.gov/pubmed/26628925
http://dx.doi.org/10.1186/s13321-015-0105-3
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author Mussa, Hamse Y.
Mitchell, John B. O.
Glen, Robert C.
author_facet Mussa, Hamse Y.
Mitchell, John B. O.
Glen, Robert C.
author_sort Mussa, Hamse Y.
collection PubMed
description It is common in cheminformatics to represent the properties of a ligand as a string of 1’s and 0’s, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant features are binary, they inevitably lead to a classifier capable of capturing only a linear relationship between structural features and activity. If, instead, we were to use relevant but non-redundant real-valued features, the resulting predictive model would be capable of describing a non-linear structure-activity relationship. Hence, we suggest that real-valued features, where available, are to be preferred in this scenario.
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spelling pubmed-46658942015-12-02 A note on utilising binary features as ligand descriptors Mussa, Hamse Y. Mitchell, John B. O. Glen, Robert C. J Cheminform Commentary It is common in cheminformatics to represent the properties of a ligand as a string of 1’s and 0’s, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant features are binary, they inevitably lead to a classifier capable of capturing only a linear relationship between structural features and activity. If, instead, we were to use relevant but non-redundant real-valued features, the resulting predictive model would be capable of describing a non-linear structure-activity relationship. Hence, we suggest that real-valued features, where available, are to be preferred in this scenario. Springer International Publishing 2015-12-01 /pmc/articles/PMC4665894/ /pubmed/26628925 http://dx.doi.org/10.1186/s13321-015-0105-3 Text en © Mussa et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Commentary
Mussa, Hamse Y.
Mitchell, John B. O.
Glen, Robert C.
A note on utilising binary features as ligand descriptors
title A note on utilising binary features as ligand descriptors
title_full A note on utilising binary features as ligand descriptors
title_fullStr A note on utilising binary features as ligand descriptors
title_full_unstemmed A note on utilising binary features as ligand descriptors
title_short A note on utilising binary features as ligand descriptors
title_sort note on utilising binary features as ligand descriptors
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4665894/
https://www.ncbi.nlm.nih.gov/pubmed/26628925
http://dx.doi.org/10.1186/s13321-015-0105-3
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