Advancing drug discovery with deep attention neural networks.
Drug Discov Today
; 29(8): 104067, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38925473
ABSTRACT
In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Descubrimiento de Drogas
Límite:
Humans
Idioma:
En
Revista:
Drug Discov Today
Asunto de la revista:
FARMACOLOGIA
/
TERAPIA POR MEDICAMENTOS
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido