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Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2
Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the “COVID Moonshot” project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901841/ https://www.ncbi.nlm.nih.gov/pubmed/36778642 http://dx.doi.org/10.1007/s13721-023-00410-9 |
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author | Gousiadou, Chrysoula Doganis, Philip Sarimveis, Haralambos |
author_facet | Gousiadou, Chrysoula Doganis, Philip Sarimveis, Haralambos |
author_sort | Gousiadou, Chrysoula |
collection | PubMed |
description | Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the “COVID Moonshot” project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13721-023-00410-9. |
format | Online Article Text |
id | pubmed-9901841 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-99018412023-02-07 Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 Gousiadou, Chrysoula Doganis, Philip Sarimveis, Haralambos Netw Model Anal Health Inform Bioinform Original Article Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the “COVID Moonshot” project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13721-023-00410-9. Springer Vienna 2023-02-06 2023 /pmc/articles/PMC9901841/ /pubmed/36778642 http://dx.doi.org/10.1007/s13721-023-00410-9 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/) . |
spellingShingle | Original Article Gousiadou, Chrysoula Doganis, Philip Sarimveis, Haralambos Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title | Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title_full | Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title_fullStr | Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title_full_unstemmed | Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title_short | Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2 |
title_sort | development of artificial neural network models to predict the pampa effective permeability of new, orally administered drugs active against the coronavirus sars-cov-2 |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901841/ https://www.ncbi.nlm.nih.gov/pubmed/36778642 http://dx.doi.org/10.1007/s13721-023-00410-9 |
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