Deep-learning- and pharmacophore-based prediction of RAGE inhibitors.
Phys Biol
; 17(3): 036003, 2020 03 16.
Article
en En
| MEDLINE
| ID: mdl-31905346
ABSTRACT
The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, multiple hypothesized binding modes, and small amounts of activity data. The main objective of this study was to demonstrate the efficacy of deep-learning (DL) techniques on small bioactivity datasets, and to identify candidate inhibitors of RAGE. We applied transfer learning in the form of a semi-supervised molecular representation in order to address small dataset problems. To validate the candidate inhibitors, we examined them using more computationally expensive pharmacophore-modeling and docking techniques. We created a strong classifier of RAGE activity, producing 79 candidate inhibitors. These candidates agreed with docking models and were shown to have no significant statistical difference from pharmacophore-based results. The transfer-learning techniques used allow DL to generalize chemical features from small bioactivity datasets to a broader library of compounds with high accuracy. Furthermore, the DL model is able to handle multiple binding modes without explicit instructions. Our results demonstrate the potential of a broad family of DL techniques on bioactivity predictions.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Bibliotecas de Moléculas Pequeñas
/
Receptor para Productos Finales de Glicación Avanzada
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Phys Biol
Asunto de la revista:
BIOLOGIA
Año:
2020
Tipo del documento:
Article