Feb 16, 2024 · The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph ...
The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations ...
Feb 12, 2024 · This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically ...
Oct 3, 2019 · In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions.
TDGraphDTA are introduced to predict drug–target interactions using multi-scale information interaction and graph optimization.
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Table 5 Prediction performance on the Davis dataset. From: Drug–target affinity prediction with extended graph learning-convolutional networks. Model. MSE. Cl.
We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity.
Sep 16, 2024 · In this paper, we design a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages.
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Jun 1, 2020 · Drug–target affinity (DTA) prediction is an important step in virtual screening, which can quickly match target and drug and speed up the process of drug ...
Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is proposed for DTA prediction, which could predict the ...