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Using Jupyter Notebooks for re-training machine learning models

Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a...

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Detalles Bibliográficos
Autores principales: Smajić, Aljoša, Grandits, Melanie, Ecker, Gerhard F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375336/
https://www.ncbi.nlm.nih.gov/pubmed/35964049
http://dx.doi.org/10.1186/s13321-022-00635-2
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author Smajić, Aljoša
Grandits, Melanie
Ecker, Gerhard F.
author_facet Smajić, Aljoša
Grandits, Melanie
Ecker, Gerhard F.
author_sort Smajić, Aljoša
collection PubMed
description Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.
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spelling pubmed-93753362022-08-14 Using Jupyter Notebooks for re-training machine learning models Smajić, Aljoša Grandits, Melanie Ecker, Gerhard F. J Cheminform Educational Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach. Springer International Publishing 2022-08-13 /pmc/articles/PMC9375336/ /pubmed/35964049 http://dx.doi.org/10.1186/s13321-022-00635-2 Text en © The Author(s) 2022 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 Educational
Smajić, Aljoša
Grandits, Melanie
Ecker, Gerhard F.
Using Jupyter Notebooks for re-training machine learning models
title Using Jupyter Notebooks for re-training machine learning models
title_full Using Jupyter Notebooks for re-training machine learning models
title_fullStr Using Jupyter Notebooks for re-training machine learning models
title_full_unstemmed Using Jupyter Notebooks for re-training machine learning models
title_short Using Jupyter Notebooks for re-training machine learning models
title_sort using jupyter notebooks for re-training machine learning models
topic Educational
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375336/
https://www.ncbi.nlm.nih.gov/pubmed/35964049
http://dx.doi.org/10.1186/s13321-022-00635-2
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