Prediction of drug–target binding affinity using similarity-based convolutional neural network
Abstract Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful...
Guardado en:
Autores principales: | Jooyong Shim, Zhen-Yu Hong, Insuk Sohn, Changha Hwang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/778c43bd612a4401b8e5e54df503addb |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Protein-ligand binding affinity prediction model based on graph attention network
por: Hong Yuan, et al.
Publicado: (2021) -
Similar neural responses predict friendship
por: Carolyn Parkinson, et al.
Publicado: (2018) -
Network neighbors of drug targets contribute to drug side-effect similarity.
por: Lucas Brouwers, et al.
Publicado: (2011) -
The predictive skill of convolutional neural networks models for disease forecasting.
por: Kookjin Lee, et al.
Publicado: (2021) -
Structure-based protein–ligand interaction fingerprints for binding affinity prediction
por: Debby D. Wang, et al.
Publicado: (2021)