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Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations
Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042106/ https://www.ncbi.nlm.nih.gov/pubmed/33846469 http://dx.doi.org/10.1038/s41598-021-87042-z |
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author | Feldmann, Christian Bajorath, Jürgen |
author_facet | Feldmann, Christian Bajorath, Jürgen |
author_sort | Feldmann, Christian |
collection | PubMed |
description | Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not generalizable. Feature weighting and mapping identified characteristic substructures in test compounds. Taken together, these findings are relevant for the design of compounds with desired multi-target activity. |
format | Online Article Text |
id | pubmed-8042106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80421062021-04-14 Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations Feldmann, Christian Bajorath, Jürgen Sci Rep Article Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not generalizable. Feature weighting and mapping identified characteristic substructures in test compounds. Taken together, these findings are relevant for the design of compounds with desired multi-target activity. Nature Publishing Group UK 2021-04-12 /pmc/articles/PMC8042106/ /pubmed/33846469 http://dx.doi.org/10.1038/s41598-021-87042-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Feldmann, Christian Bajorath, Jürgen Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title | Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title_full | Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title_fullStr | Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title_full_unstemmed | Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title_short | Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
title_sort | machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042106/ https://www.ncbi.nlm.nih.gov/pubmed/33846469 http://dx.doi.org/10.1038/s41598-021-87042-z |
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