Artículo
The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking
Fecha de publicación:
04/2023
Editorial:
American Chemical Society
Revista:
Journal of Chemical Information and Modeling
ISSN:
1549-9596
e-ISSN:
1549-960X
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.
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Articulos(IIMT)
Articulos de INSTITUTO DE INVESTIGACIONES EN MEDICINA TRASLACIONAL
Articulos de INSTITUTO DE INVESTIGACIONES EN MEDICINA TRASLACIONAL
Citación
Cavasotto, Claudio Norberto; Di Filippo, Juan Ignacio; The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking; American Chemical Society; Journal of Chemical Information and Modeling; 63; 8; 4-2023; 2267-2280
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