In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy.
Sci Rep
; 14(1): 16562, 2024 07 17.
Article
en En
| MEDLINE
| ID: mdl-39020064
ABSTRACT
Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and significant challenge. Drug repurposing emerges as a cost-effective and efficient strategy to combat this disorder. This study leverages the transformer-based deep learning methods coupled with molecular binding affinity calculation to develop a novel in-silico drug repurposing pipeline for epilepsy. The number of candidate inhibitors against 24 target proteins encoded by gain-of-function genes implicated in epileptogenesis ranged from zero to several hundreds. Our pipeline has repurposed the medications with most anti-epileptic drugs and nearly half psychiatric medications, highlighting the effectiveness of our pipeline. Furthermore, Lomitapide, a cholesterol-lowering drug, first emerged as particularly noteworthy, exhibiting high binding affinity for 10 targets and verified by molecular dynamics simulation and mechanism analysis. These findings provided a novel perspective on therapeutic strategies for other central nervous system disease.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Epilepsia
/
Simulación de Dinámica Molecular
/
Reposicionamiento de Medicamentos
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Aprendizaje Profundo
/
Anticonvulsivantes
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido