Explainable machine learning predictions of dual-target compounds reveal characteristic structural features

Abstract Compounds with defined multi-target activity play an increasingly important role in drug discovery. Structural features that might be signatures of such compounds have mostly remained elusive thus far. We have explored the potential of explainable machine learning to uncover structural moti...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Christian Feldmann, Maren Philipps, Jürgen Bajorath
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e417e0653e004604a6e4ace6cc0f33c4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e417e0653e004604a6e4ace6cc0f33c4
record_format	dspace
spelling	oai:doaj.org-article:e417e0653e004604a6e4ace6cc0f33c42021-11-08T10:55:12ZExplainable machine learning predictions of dual-target compounds reveal characteristic structural features10.1038/s41598-021-01099-42045-2322https://doaj.org/article/e417e0653e004604a6e4ace6cc0f33c42021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01099-4https://doaj.org/toc/2045-2322Abstract Compounds with defined multi-target activity play an increasingly important role in drug discovery. Structural features that might be signatures of such compounds have mostly remained elusive thus far. We have explored the potential of explainable machine learning to uncover structural motifs that are characteristic of dual-target compounds. For a pharmacologically relevant target pair-based test system designed for our study, accurate prediction models were derived and the influence of molecular representation features of test compounds was quantified to explain the predictions. The analysis revealed small numbers of specific features whose presence in dual-target and absence in single-target compounds determined accurate predictions. These features formed coherent substructures in dual-target compounds. From computational analysis of specific feature contributions, structural motifs emerged that were confirmed to be signatures of different dual-target activities. Our findings demonstrate the ability of explainable machine learning to bridge between predictions and intuitive chemical analysis and reveal characteristic substructures of dual-target compounds.Christian FeldmannMaren PhilippsJürgen BajorathNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christian Feldmann Maren Philipps Jürgen Bajorath Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
description	Abstract Compounds with defined multi-target activity play an increasingly important role in drug discovery. Structural features that might be signatures of such compounds have mostly remained elusive thus far. We have explored the potential of explainable machine learning to uncover structural motifs that are characteristic of dual-target compounds. For a pharmacologically relevant target pair-based test system designed for our study, accurate prediction models were derived and the influence of molecular representation features of test compounds was quantified to explain the predictions. The analysis revealed small numbers of specific features whose presence in dual-target and absence in single-target compounds determined accurate predictions. These features formed coherent substructures in dual-target compounds. From computational analysis of specific feature contributions, structural motifs emerged that were confirmed to be signatures of different dual-target activities. Our findings demonstrate the ability of explainable machine learning to bridge between predictions and intuitive chemical analysis and reveal characteristic substructures of dual-target compounds.
format	article
author	Christian Feldmann Maren Philipps Jürgen Bajorath
author_facet	Christian Feldmann Maren Philipps Jürgen Bajorath
author_sort	Christian Feldmann
title	Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
title_short	Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
title_full	Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
title_fullStr	Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
title_full_unstemmed	Explainable machine learning predictions of dual-target compounds reveal characteristic structural features
title_sort	explainable machine learning predictions of dual-target compounds reveal characteristic structural features
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e417e0653e004604a6e4ace6cc0f33c4
work_keys_str_mv	AT christianfeldmann explainablemachinelearningpredictionsofdualtargetcompoundsrevealcharacteristicstructuralfeatures AT marenphilipps explainablemachinelearningpredictionsofdualtargetcompoundsrevealcharacteristicstructuralfeatures AT jurgenbajorath explainablemachinelearningpredictionsofdualtargetcompoundsrevealcharacteristicstructuralfeatures
_version_	1718442575672639488

Explainable machine learning predictions of dual-target compounds reveal characteristic structural features

Ejemplares similares