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Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs
Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811035/ https://www.ncbi.nlm.nih.gov/pubmed/35127645 http://dx.doi.org/10.3389/fchem.2021.787194 |
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author | Alves, Luiz Anastacio Ferreira, Natiele Carla da Silva Maricato, Victor Alberto, Anael Viana Pinto Dias, Evellyn Araujo Jose Aguiar Coelho, Nt |
author_facet | Alves, Luiz Anastacio Ferreira, Natiele Carla da Silva Maricato, Victor Alberto, Anael Viana Pinto Dias, Evellyn Araujo Jose Aguiar Coelho, Nt |
author_sort | Alves, Luiz Anastacio |
collection | PubMed |
description | Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome. |
format | Online Article Text |
id | pubmed-8811035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88110352022-02-04 Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs Alves, Luiz Anastacio Ferreira, Natiele Carla da Silva Maricato, Victor Alberto, Anael Viana Pinto Dias, Evellyn Araujo Jose Aguiar Coelho, Nt Front Chem Chemistry Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome. Frontiers Media S.A. 2022-01-20 /pmc/articles/PMC8811035/ /pubmed/35127645 http://dx.doi.org/10.3389/fchem.2021.787194 Text en Copyright © 2022 Alves, Ferreira, Maricato, Alberto, Dias and Jose Aguiar Coelho. 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 Alves, Luiz Anastacio Ferreira, Natiele Carla da Silva Maricato, Victor Alberto, Anael Viana Pinto Dias, Evellyn Araujo Jose Aguiar Coelho, Nt Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title | Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title_full | Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title_fullStr | Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title_full_unstemmed | Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title_short | Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs |
title_sort | graph neural networks as a potential tool in improving virtual screening programs |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811035/ https://www.ncbi.nlm.nih.gov/pubmed/35127645 http://dx.doi.org/10.3389/fchem.2021.787194 |
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