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Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistanc...
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/PMC8203334/ https://www.ncbi.nlm.nih.gov/pubmed/34135888 http://dx.doi.org/10.3389/fimmu.2021.642383 |
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author | Moreira-Filho, José T. Silva, Arthur C. Dantas, Rafael F. Gomes, Barbara F. Souza Neto, Lauro R. Brandao-Neto, Jose Owens, Raymond J. Furnham, Nicholas Neves, Bruno J. Silva-Junior, Floriano P. Andrade, Carolina H. |
author_facet | Moreira-Filho, José T. Silva, Arthur C. Dantas, Rafael F. Gomes, Barbara F. Souza Neto, Lauro R. Brandao-Neto, Jose Owens, Raymond J. Furnham, Nicholas Neves, Bruno J. Silva-Junior, Floriano P. Andrade, Carolina H. |
author_sort | Moreira-Filho, José T. |
collection | PubMed |
description | Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor. |
format | Online Article Text |
id | pubmed-8203334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82033342021-06-15 Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence Moreira-Filho, José T. Silva, Arthur C. Dantas, Rafael F. Gomes, Barbara F. Souza Neto, Lauro R. Brandao-Neto, Jose Owens, Raymond J. Furnham, Nicholas Neves, Bruno J. Silva-Junior, Floriano P. Andrade, Carolina H. Front Immunol Immunology Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor. Frontiers Media S.A. 2021-05-31 /pmc/articles/PMC8203334/ /pubmed/34135888 http://dx.doi.org/10.3389/fimmu.2021.642383 Text en Copyright © 2021 Moreira-Filho, Silva, Dantas, Gomes, Souza Neto, Brandao-Neto, Owens, Furnham, Neves, Silva-Junior and Andrade 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 | Immunology Moreira-Filho, José T. Silva, Arthur C. Dantas, Rafael F. Gomes, Barbara F. Souza Neto, Lauro R. Brandao-Neto, Jose Owens, Raymond J. Furnham, Nicholas Neves, Bruno J. Silva-Junior, Floriano P. Andrade, Carolina H. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title | Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title_full | Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title_fullStr | Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title_full_unstemmed | Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title_short | Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence |
title_sort | schistosomiasis drug discovery in the era of automation and artificial intelligence |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203334/ https://www.ncbi.nlm.nih.gov/pubmed/34135888 http://dx.doi.org/10.3389/fimmu.2021.642383 |
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