Deep learning-driven research for drug discovery
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/116630 |
Resumo: | Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates. |
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Deep learning-driven research for drug discoverytackling malariaModelling and SimulationComputational Theory and MathematicsInfectious DiseasesDrug DiscoverySDG 3 - Good Health and Well-beingMalaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.Vector borne diseases and pathogens (VBD)Global Health and Tropical Medicine (GHTM)Instituto de Higiene e Medicina Tropical (IHMT)RUNNeves, Bruno J.Braga, Rodolpho C.Alves, Vinicius M.Lima, Marília N.N.Cassiano, Gustavo C.Muratov, Eugene N.Costa, Fabio T.M.Andrade, Carolina Horta2021-05-01T22:52:59Z2020-02-182020-02-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/116630eng1553-734XPURE: 26684235https://doi.org/10.1371/journal.pcbi.1007025info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:59:18Zoai:run.unl.pt:10362/116630Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:10.922537Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep learning-driven research for drug discovery tackling malaria |
title |
Deep learning-driven research for drug discovery |
spellingShingle |
Deep learning-driven research for drug discovery Neves, Bruno J. Modelling and Simulation Computational Theory and Mathematics Infectious Diseases Drug Discovery SDG 3 - Good Health and Well-being |
title_short |
Deep learning-driven research for drug discovery |
title_full |
Deep learning-driven research for drug discovery |
title_fullStr |
Deep learning-driven research for drug discovery |
title_full_unstemmed |
Deep learning-driven research for drug discovery |
title_sort |
Deep learning-driven research for drug discovery |
author |
Neves, Bruno J. |
author_facet |
Neves, Bruno J. Braga, Rodolpho C. Alves, Vinicius M. Lima, Marília N.N. Cassiano, Gustavo C. Muratov, Eugene N. Costa, Fabio T.M. Andrade, Carolina Horta |
author_role |
author |
author2 |
Braga, Rodolpho C. Alves, Vinicius M. Lima, Marília N.N. Cassiano, Gustavo C. Muratov, Eugene N. Costa, Fabio T.M. Andrade, Carolina Horta |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Vector borne diseases and pathogens (VBD) Global Health and Tropical Medicine (GHTM) Instituto de Higiene e Medicina Tropical (IHMT) RUN |
dc.contributor.author.fl_str_mv |
Neves, Bruno J. Braga, Rodolpho C. Alves, Vinicius M. Lima, Marília N.N. Cassiano, Gustavo C. Muratov, Eugene N. Costa, Fabio T.M. Andrade, Carolina Horta |
dc.subject.por.fl_str_mv |
Modelling and Simulation Computational Theory and Mathematics Infectious Diseases Drug Discovery SDG 3 - Good Health and Well-being |
topic |
Modelling and Simulation Computational Theory and Mathematics Infectious Diseases Drug Discovery SDG 3 - Good Health and Well-being |
description |
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-18 2020-02-18T00:00:00Z 2021-05-01T22:52:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/116630 |
url |
http://hdl.handle.net/10362/116630 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1553-734X PURE: 26684235 https://doi.org/10.1371/journal.pcbi.1007025 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
21 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138041924681728 |