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Papyrus: a large-scale curated dataset aimed at bioactivity predictions
With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is ne...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824924/ https://www.ncbi.nlm.nih.gov/pubmed/36609528 http://dx.doi.org/10.1186/s13321-022-00672-x |
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author | Béquignon, O. J. M. Bongers, B. J. Jespers, W. IJzerman, A. P. van der Water, B. van Westen, G. J. P. |
author_facet | Béquignon, O. J. M. Bongers, B. J. Jespers, W. IJzerman, A. P. van der Water, B. van Westen, G. J. P. |
author_sort | Béquignon, O. J. M. |
collection | PubMed |
description | With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure–activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00672-x. |
format | Online Article Text |
id | pubmed-9824924 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98249242023-01-08 Papyrus: a large-scale curated dataset aimed at bioactivity predictions Béquignon, O. J. M. Bongers, B. J. Jespers, W. IJzerman, A. P. van der Water, B. van Westen, G. J. P. J Cheminform Research With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure–activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00672-x. Springer International Publishing 2023-01-06 /pmc/articles/PMC9824924/ /pubmed/36609528 http://dx.doi.org/10.1186/s13321-022-00672-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Béquignon, O. J. M. Bongers, B. J. Jespers, W. IJzerman, A. P. van der Water, B. van Westen, G. J. P. Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title | Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title_full | Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title_fullStr | Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title_full_unstemmed | Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title_short | Papyrus: a large-scale curated dataset aimed at bioactivity predictions |
title_sort | papyrus: a large-scale curated dataset aimed at bioactivity predictions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824924/ https://www.ncbi.nlm.nih.gov/pubmed/36609528 http://dx.doi.org/10.1186/s13321-022-00672-x |
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