Cargando…
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction. Our proposed model is inspired by sentence classificatio...
Autores principales: | Yazdani-Jahromi, Mehdi, Yousefi, Niloofar, Tayebi, Aida, Kolanthai, Elayaraja, Neal, Craig J, Seal, Sudipta, Garibay, Ozlem Ozmen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294423/ https://www.ncbi.nlm.nih.gov/pubmed/35817396 http://dx.doi.org/10.1093/bib/bbac272 |
Ejemplares similares
-
BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing
por: Yousefi, Niloofar, et al.
Publicado: (2023) -
UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning
por: Tayebi, Aida, et al.
Publicado: (2022) -
An inductive graph neural network model for compound–protein interaction prediction based on a homogeneous graph
por: Wan, Xiaozhe, et al.
Publicado: (2022) -
cropCSM: designing safe and potent herbicides with graph-based signatures
por: Pires, Douglas E V, et al.
Publicado: (2022) -
Ensembles of knowledge graph embedding models improve predictions for drug discovery
por: Rivas-Barragan, Daniel, et al.
Publicado: (2022)