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Artículo

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

Ponzoni, IgnacioIcon ; Sebastián Pérez, Víctor; Requena Triguero, Carlos; Roca, Carlos; Martínez, María JimenaIcon ; Cravero, FiorellaIcon ; Diaz, Monica FatimaIcon ; Páez, Juan A.; Gómez Arrayás, Ramón; Adrio, Javier; Campillo, Nuria E.
Fecha de publicación: 25/05/2017
Editorial: Nature Publishing Group
Revista: Scientific Reports
ISSN: 2045-2322
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

Quantitative structure–activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances. Beyond the contrastive analysis of feature selection and feature learning methods as competitive approaches, the hybridization of these strategies is also evaluated based on previous results obtained in material sciences. From the experimental results, it can be concluded that there is not a clear winner between both approaches because the performance depends on the characteristics of the compound databases used for modeling. Nevertheless, in several cases, it was observed that the accuracy of the models can be improved by combining both approaches when the molecular descriptor sets provided by feature selection and feature learning contain complementary information.
Palabras clave: Machine Learning , Qsar , Feature Selection , Feature Learning
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution 2.5 Unported (CC BY 2.5)
Identificadores
URI: http://hdl.handle.net/11336/45651
URL: https://www.nature.com/articles/s41598-017-02114-3
DOI: http://dx.doi.org/10.1038/s41598-017-02114-3
Colecciones
Articulos(PLAPIQUI)
Articulos de PLANTA PILOTO DE INGENIERIA QUIMICA (I)
Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Ponzoni, Ignacio; Sebastián Pérez, Víctor; Requena Triguero, Carlos; Roca, Carlos; Martínez, María Jimena; et al.; Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery; Nature Publishing Group; Scientific Reports; 7; 1; 25-5-2017; 1-19
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