Comparative study of classification algorithms using molecular descriptors in toxicological databases
Autor(a) principal: | |
---|---|
Data de Publicação: | 2009 |
Outros Autores: | , , , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://repositorio-aberto.up.pt/handle/10216/73608 |
Resumo: | The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models. |
id |
RCAP_540a588270cdb105400cf1c64f9dae7a |
---|---|
oai_identifier_str |
oai:repositorio-aberto.up.pt:10216/73608 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Comparative study of classification algorithms using molecular descriptors in toxicological databasesCiência de computadores, Ciências farmacológicas, Ciências da computação e da informaçãoComputer science, Pharmacological sciences, Computer and information sciencesThe rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.20092009-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/73608eng10.1007/978-3-642-03223-3_11Max PereiraVítor Santos CostaRui CamachoNuno A. FonsecaCarlos SimõesRui M. M. Britoinfo: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:RCAAP2023-11-29T13:58:13Zoai:repositorio-aberto.up.pt:10216/73608Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:51:07.596008Repositório 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 |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
title |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
spellingShingle |
Comparative study of classification algorithms using molecular descriptors in toxicological databases Max Pereira Ciência de computadores, Ciências farmacológicas, Ciências da computação e da informação Computer science, Pharmacological sciences, Computer and information sciences |
title_short |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
title_full |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
title_fullStr |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
title_full_unstemmed |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
title_sort |
Comparative study of classification algorithms using molecular descriptors in toxicological databases |
author |
Max Pereira |
author_facet |
Max Pereira Vítor Santos Costa Rui Camacho Nuno A. Fonseca Carlos Simões Rui M. M. Brito |
author_role |
author |
author2 |
Vítor Santos Costa Rui Camacho Nuno A. Fonseca Carlos Simões Rui M. M. Brito |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Max Pereira Vítor Santos Costa Rui Camacho Nuno A. Fonseca Carlos Simões Rui M. M. Brito |
dc.subject.por.fl_str_mv |
Ciência de computadores, Ciências farmacológicas, Ciências da computação e da informação Computer science, Pharmacological sciences, Computer and information sciences |
topic |
Ciência de computadores, Ciências farmacológicas, Ciências da computação e da informação Computer science, Pharmacological sciences, Computer and information sciences |
description |
The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009 2009-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio-aberto.up.pt/handle/10216/73608 |
url |
https://repositorio-aberto.up.pt/handle/10216/73608 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-3-642-03223-3_11 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799135831008477184 |