NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction
H He, G Chen, CYC Chen - Bioinformatics, 2023 - academic.oup.com
Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …
drug discovery. In recent years, machine learning algorithms have made great progress in …
NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction.
H He, G Chen, CYC Chen - Bioinformatics, 2023 - search.ebscohost.com
Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …
drug discovery. In recent years, machine learning algorithms have made great progress in …
NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction.
H He, G Chen, CY Chen - Bioinformatics (Oxford, England), 2023 - europepmc.org
Results In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for
interpretable DTA prediction. It can adaptively acquire feature representations of drugs and …
interpretable DTA prediction. It can adaptively acquire feature representations of drugs and …
[PDF][PDF] NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction
H He, G Chen, CYC Chen - Bioinformatics, 2023 - scienceopen.com
Motivation: Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …
drug discovery. In recent years, machine learning algorithms have made great progress in …
NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction
H He, G Chen, CYC Chen - Bioinformatics (Oxford …, 2023 - pubmed.ncbi.nlm.nih.gov
Motivation Large-scale prediction of drug-target affinity (DTA) plays an important role in drug
discovery. In recent years, machine learning algorithms have made great progress in DTA …
discovery. In recent years, machine learning algorithms have made great progress in DTA …
[PDF][PDF] NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction
H He, G Chen, CYC Chen - Bioinformatics, 2023 - academic.oup.com
Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …
drug discovery. In recent years, machine learning algorithms have made great progress in …
[HTML][HTML] NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction
H He, G Chen, CYC Chen - Bioinformatics, 2023 - ncbi.nlm.nih.gov
Results In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for
interpretable DTA prediction. It can adaptively acquire feature representations of drugs and …
interpretable DTA prediction. It can adaptively acquire feature representations of drugs and …
NHGNN-DTA: A Node-adaptive Hybrid Graph Neural Network for Interpretable Drug-target Binding Affinity Prediction.
H He, G Chen, CY Chen - Bioinformatics (Oxford, England), 2023 - europepmc.org
Large-scale prediction of drug-target affinity (DTA) plays an important role in drug discovery.
In recent years, machine learning algorithms have made great progress in DTA prediction by …
In recent years, machine learning algorithms have made great progress in DTA prediction by …