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

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

Abstract 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 appro...

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Autores principales: Ignacio Ponzoni, Víctor Sebastián-Pérez, Carlos Requena-Triguero, Carlos Roca, María J. Martínez, Fiorella Cravero, Mónica F. Díaz, Juan A. Páez, Ramón Gómez Arrayás, Javier Adrio, Nuria E. Campillo
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/d95973b8023d4de68ded65e398e04d19
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https://doaj.org/article/d95973b8023d4de68ded65e398e04d19

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