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FP-ADMET: a compendium of fingerprint-based ADMET prediction models
MOTIVATION: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limit...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer International Publishing
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479898/ https://www.ncbi.nlm.nih.gov/pubmed/34583740 http://dx.doi.org/10.1186/s13321-021-00557-5 |
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author | Venkatraman, Vishwesh |
author_facet | Venkatraman, Vishwesh |
author_sort | Venkatraman, Vishwesh |
collection | PubMed |
description | MOTIVATION: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. SUMMARY: In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY: The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00557-5. |
format | Online Article Text |
id | pubmed-8479898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84798982021-09-29 FP-ADMET: a compendium of fingerprint-based ADMET prediction models Venkatraman, Vishwesh J Cheminform Research Article MOTIVATION: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. SUMMARY: In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY: The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00557-5. Springer International Publishing 2021-09-28 /pmc/articles/PMC8479898/ /pubmed/34583740 http://dx.doi.org/10.1186/s13321-021-00557-5 Text en © The Author(s) 2021 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 Article Venkatraman, Vishwesh FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title | FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title_full | FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title_fullStr | FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title_full_unstemmed | FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title_short | FP-ADMET: a compendium of fingerprint-based ADMET prediction models |
title_sort | fp-admet: a compendium of fingerprint-based admet prediction models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479898/ https://www.ncbi.nlm.nih.gov/pubmed/34583740 http://dx.doi.org/10.1186/s13321-021-00557-5 |
work_keys_str_mv | AT venkatramanvishwesh fpadmetacompendiumoffingerprintbasedadmetpredictionmodels |