Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations

Abstract 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 cha...

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Autores principales:	Christian Feldmann, Jürgen Bajorath
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/83b7d8f712e044dbb9f8249f70d76be9
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spelling	oai:doaj.org-article:83b7d8f712e044dbb9f8249f70d76be92021-12-02T15:51:16ZMachine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations10.1038/s41598-021-87042-z2045-2322https://doaj.org/article/83b7d8f712e044dbb9f8249f70d76be92021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87042-zhttps://doaj.org/toc/2045-2322Abstract 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.Christian FeldmannJürgen BajorathNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christian Feldmann Jürgen Bajorath Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations
description	Abstract 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	article
author	Christian Feldmann Jürgen Bajorath
author_facet	Christian Feldmann Jürgen Bajorath
author_sort	Christian Feldmann
title	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_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_sort	machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/83b7d8f712e044dbb9f8249f70d76be9
work_keys_str_mv	AT christianfeldmann machinelearningrevealsthatstructuralfeaturesdistinguishingpromiscuousandnonpromiscuouscompoundsdependontargetcombinations AT jurgenbajorath machinelearningrevealsthatstructuralfeaturesdistinguishingpromiscuousandnonpromiscuouscompoundsdependontargetcombinations
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Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations

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