Cargando…
GANsDTA: Predicting Drug-Target Binding Affinity Using GANs
The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label information. However, in biomedicine, obtaining labele...
Autores principales: | Zhao, Lingling, Wang, Junjie, Pang, Long, Liu, Yang, Zhang, Jun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962343/ https://www.ncbi.nlm.nih.gov/pubmed/31993067 http://dx.doi.org/10.3389/fgene.2019.01243 |
Ejemplares similares
-
DataDTA: a multi-feature and dual-interaction aggregation framework for drug–target binding affinity prediction
por: Zhu, Yan, et al.
Publicado: (2023) -
DeepDTA: deep drug–target binding affinity prediction
por: Öztürk, Hakime, et al.
Publicado: (2018) -
CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention
por: Ghimire, Ashutosh, et al.
Publicado: (2022) -
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
por: Yang, Ziduo, et al.
Publicado: (2022) -
ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding
por: Wang, Junjie, et al.
Publicado: (2022)