Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.
Molecules
; 28(8)2023 Apr 20.
Article
en En
| MEDLINE
| ID: mdl-37110831
ABSTRACT
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and ß-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Acetilcolinesterasa
/
Enfermedad de Alzheimer
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Molecules
Asunto de la revista:
BIOLOGIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Vietnam