Artículo

Deep Learning to Therapeutically Target Unreported Complexes

Fernandez, ArielIcon
Fecha de publicación: 21/05/2019
Editorial: Elsevier Science London
Revista: Trends In Pharmacological Sciences
ISSN: 0165-6147
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:

Resumen

The disruption of large protein–protein (PP) interfaces remains a challenge in targeted therapy. Designing drugs that compete with binding partners is daunting, especially when the structure of the protein complex is unknown. To address the problem we propose a deep protein databank (PDB) learning platform to discover targetable epitopes for complex-disruptive leads.
Palabras clave: DEEP LEARNING , PROTEIN COMPLEX , PROTEIN COMPLEX DISRUPTION , PROTEIN DATA BANK , TARGETED MOLECULAR THERAPY , UNKNOWN PROTEIN STRUCTURE
 
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/125003
DOI: http://dx.doi.org/10.1016/j.tips.2019.04.009
URL: https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(19)3008
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Articulos(INQUISUR)
Articulos de INST.DE QUIMICA DEL SUR
Citación
Fernandez, Ariel; Deep Learning to Therapeutically Target Unreported Complexes; Elsevier Science London; Trends In Pharmacological Sciences; 40; 8; 21-5-2019; 551-554
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