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Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics
Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target protein...
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/PMC8270985/ https://www.ncbi.nlm.nih.gov/pubmed/34244588 http://dx.doi.org/10.1038/s41598-021-93771-y |
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author | Rodríguez-Pérez, Raquel Bajorath, Jürgen |
author_facet | Rodríguez-Pérez, Raquel Bajorath, Jürgen |
author_sort | Rodríguez-Pérez, Raquel |
collection | PubMed |
description | Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target proteins. On the basis of a large-scale analysis generating and comparing machine learning models for more than 200 proteins, feature importance correlation analysis is shown to detect similar compound binding characteristics. Furthermore, rather unexpectedly, the analysis also reveals functional relationships between proteins that are independent of active compounds and binding characteristics. Feature importance correlation analysis does not depend on specific representations, algorithms, or metrics and is generally applicable as long as predictive models can be derived. Moreover, the approach does not require or involve explainable or interpretable machine learning, but only access to feature weights or importance values. On the basis of our findings, the approach represents a new facet of machine learning in drug discovery with potential for practical applications. |
format | Online Article Text |
id | pubmed-8270985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82709852021-07-13 Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics Rodríguez-Pérez, Raquel Bajorath, Jürgen Sci Rep Article Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target proteins. On the basis of a large-scale analysis generating and comparing machine learning models for more than 200 proteins, feature importance correlation analysis is shown to detect similar compound binding characteristics. Furthermore, rather unexpectedly, the analysis also reveals functional relationships between proteins that are independent of active compounds and binding characteristics. Feature importance correlation analysis does not depend on specific representations, algorithms, or metrics and is generally applicable as long as predictive models can be derived. Moreover, the approach does not require or involve explainable or interpretable machine learning, but only access to feature weights or importance values. On the basis of our findings, the approach represents a new facet of machine learning in drug discovery with potential for practical applications. Nature Publishing Group UK 2021-07-09 /pmc/articles/PMC8270985/ /pubmed/34244588 http://dx.doi.org/10.1038/s41598-021-93771-y 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 Rodríguez-Pérez, Raquel Bajorath, Jürgen Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title | Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title_full | Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title_fullStr | Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title_full_unstemmed | Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title_short | Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
title_sort | feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270985/ https://www.ncbi.nlm.nih.gov/pubmed/34244588 http://dx.doi.org/10.1038/s41598-021-93771-y |
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