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A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier
The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329444/ https://www.ncbi.nlm.nih.gov/pubmed/34354979 http://dx.doi.org/10.3389/fchem.2021.714678 |
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author | Di Filippo, Juan I. Bollini, Mariela Cavasotto, Claudio N. |
author_facet | Di Filippo, Juan I. Bollini, Mariela Cavasotto, Claudio N. |
author_sort | Di Filippo, Juan I. |
collection | PubMed |
description | The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns. |
format | Online Article Text |
id | pubmed-8329444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83294442021-08-04 A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier Di Filippo, Juan I. Bollini, Mariela Cavasotto, Claudio N. Front Chem Chemistry The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns. Frontiers Media S.A. 2021-07-20 /pmc/articles/PMC8329444/ /pubmed/34354979 http://dx.doi.org/10.3389/fchem.2021.714678 Text en Copyright © 2021 Di Filippo, Bollini and Cavasotto. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Di Filippo, Juan I. Bollini, Mariela Cavasotto, Claudio N. A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title | A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title_full | A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title_fullStr | A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title_full_unstemmed | A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title_short | A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier |
title_sort | machine learning model to predict drug transfer across the human placenta barrier |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329444/ https://www.ncbi.nlm.nih.gov/pubmed/34354979 http://dx.doi.org/10.3389/fchem.2021.714678 |
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