Cargando…
Decoding the protein–ligand interactions using parallel graph neural networks
Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very ti...
Autores principales: | Knutson, Carter, Bontha, Mridula, Bilbrey, Jenna A., Kumar, Neeraj |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9086424/ https://www.ncbi.nlm.nih.gov/pubmed/35538084 http://dx.doi.org/10.1038/s41598-022-10418-2 |
Ejemplares similares
-
Prediction of protein–protein interaction using graph neural networks
por: Jha, Kanchan, et al.
Publicado: (2022) -
Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences
por: Saeidi, Maham, et al.
Publicado: (2022) -
Tracking the Chemical Evolution of Iodine Species
Using Recurrent Neural Networks
por: Bilbrey, Jenna A., et al.
Publicado: (2020) -
GraphscoreDTA: optimized graph neural network for protein–ligand binding affinity prediction
por: Wang, Kaili, et al.
Publicado: (2023) -
DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein–Ligand Interaction Prediction
por: Zhang, Haiping, et al.
Publicado: (2023)