Cargando…
Graph–sequence attention and transformer for predicting drug–target affinity
Drug–target binding affinity (DTA) prediction has drawn increasing interest due to its substantial position in the drug discovery process. The development of new drugs is costly, time-consuming, and often accompanied by safety issues. Drug repurposing can avoid the expensive and lengthy process of d...
Autores principales: | Yan, Xiangfeng, Liu, Yong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562047/ https://www.ncbi.nlm.nih.gov/pubmed/36320763 http://dx.doi.org/10.1039/d2ra05566j |
Ejemplares similares
-
Drug–target affinity prediction using graph neural network and contact maps
por: Jiang, Mingjian, et al.
Publicado: (2020) -
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
por: Zhai, Haixia, et al.
Publicado: (2023) -
SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
por: Zhang, Shugang, et al.
Publicado: (2021) -
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
por: Yang, Ziduo, et al.
Publicado: (2022) -
GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity
por: Bae, Haelee, et al.
Publicado: (2022)