Cargando…
CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affini...
Autores principales: | Ghimire, Ashutosh, Tayara, Hilal, Xuan, Zhenyu, Chong, Kil To |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369082/ https://www.ncbi.nlm.nih.gov/pubmed/35955587 http://dx.doi.org/10.3390/ijms23158453 |
Ejemplares similares
-
SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
por: Zhang, Shugang, et al.
Publicado: (2021) -
Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
por: Chipofya, Mapopa, et al.
Publicado: (2021) -
DeepDTA: deep drug–target binding affinity prediction
por: Öztürk, Hakime, et al.
Publicado: (2018) -
GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity
por: Bae, Haelee, et al.
Publicado: (2022) -
GANsDTA: Predicting Drug-Target Binding Affinity Using GANs
por: Zhao, Lingling, et al.
Publicado: (2020)