QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents
The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a grea...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2015
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9032
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9032
- Palabra clave:
- Atom-based quadratic indices
Linear discriminant analysis
QSAR model
QuBiLs-MAS software
Vrtual screening, antifungal agent
Antifungal agent
Chemistry
Computer simulation
Discriminant analysis
Drug development
Quantitative structure activity relation
Statistical model
Antifungal Agents
Computer simulation
Discriminant analysis
Drug Discovery
Linear Models
Quantitative Structure-Activity Relationship
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- http://creativecommons.org/licenses/by-nc-nd/4.0/
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QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| title |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| spellingShingle |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents Atom-based quadratic indices Linear discriminant analysis QSAR model QuBiLs-MAS software Vrtual screening, antifungal agent Antifungal agent Chemistry Computer simulation Discriminant analysis Drug development Quantitative structure activity relation Statistical model Antifungal Agents Computer simulation Discriminant analysis Drug Discovery Linear Models Quantitative Structure-Activity Relationship |
| title_short |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| title_full |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| title_fullStr |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| title_full_unstemmed |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| title_sort |
QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents |
| dc.subject.keywords.none.fl_str_mv |
Atom-based quadratic indices Linear discriminant analysis QSAR model QuBiLs-MAS software Vrtual screening, antifungal agent Antifungal agent Chemistry Computer simulation Discriminant analysis Drug development Quantitative structure activity relation Statistical model Antifungal Agents Computer simulation Discriminant analysis Drug Discovery Linear Models Quantitative Structure-Activity Relationship |
| topic |
Atom-based quadratic indices Linear discriminant analysis QSAR model QuBiLs-MAS software Vrtual screening, antifungal agent Antifungal agent Chemistry Computer simulation Discriminant analysis Drug development Quantitative structure activity relation Statistical model Antifungal Agents Computer simulation Discriminant analysis Drug Discovery Linear Models Quantitative Structure-Activity Relationship |
| description |
The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections. © 2015 Taylor & Francis. |
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2015 |
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2015 |
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2020-03-26T16:32:48Z |
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2020-03-26T16:32:48Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo |
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publishedVersion |
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SAR and QSAR in Environmental Research; Vol. 26, Núm. 11; pp. 943-958 |
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1062936X |
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https://hdl.handle.net/20.500.12585/9032 |
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10.1080/1062936X.2015.1104517 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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6506280403 55665599200 55363486500 55683426700 56674579200 9434652400 57193209050 7004872108 6701762262 |
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SAR and QSAR in Environmental Research; Vol. 26, Núm. 11; pp. 943-958 1062936X 10.1080/1062936X.2015.1104517 Universidad Tecnológica de Bolívar Repositorio UTB 6506280403 55665599200 55363486500 55683426700 56674579200 9434652400 57193209050 7004872108 6701762262 |
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https://hdl.handle.net/20.500.12585/9032 |
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eng |
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eng |
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Taylor and Francis Ltd. |
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Taylor and Francis Ltd. |
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2020-03-26T16:32:48Z2020-03-26T16:32:48Z2015SAR and QSAR in Environmental Research; Vol. 26, Núm. 11; pp. 943-9581062936Xhttps://hdl.handle.net/20.500.12585/903210.1080/1062936X.2015.1104517Universidad Tecnológica de BolívarRepositorio UTB65062804035566559920055363486500556834267005667457920094346524005719320905070048721086701762262The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections. © 2015 Taylor & Francis.Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPqAntifungal AgentsRecurso electrónicoapplication/pdfengTaylor and Francis Ltd.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84947865592&doi=10.1080%2f1062936X.2015.1104517&partnerID=40&md5=9de40bde3e81a41d4828d1586f4b0c9fQuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agentsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Atom-based quadratic indicesLinear discriminant analysisQSAR modelQuBiLs-MAS softwareVrtual screening, antifungal agentAntifungal agentChemistryComputer simulationDiscriminant analysisDrug developmentQuantitative structure activity relationStatistical modelAntifungal AgentsComputer simulationDiscriminant analysisDrug DiscoveryLinear ModelsQuantitative Structure-Activity RelationshipMedina Marrero R.Marrero-Ponce Y.Barigye S.J.Echeverría Díaz Y.Acevedo Barrios, RosaCasañola-Martín G.M.García Bernal M.Torrens, F.Pérez-Giménez F.Blumberg, H.M., Jarvis, W.R., Soucie, J.M., Edwards, J.E., Patterson, J.E., Pfaller, M.A., Rangel-Frausto, M.S., Wenzel, R.P., Risk factors for candidal bloodstream infections in surgical intensive care unit patients: The NEMIS prospective multicenter study. 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