Mar 22, 2022 · In this paper, we propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA.
In this paper, we propose a novel hierarchical graph representation learning model for DTA prediction, named HGRL-DTA.
This repository contains a PyTorch implementation of the paper "Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity".
Highlights •A novel hierarchical graph representation learning model for drug-target binding affinity prediction.•Represents the drug-target binding ...
Mar 22, 2022 · In this paper, we propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL- ...
Sep 5, 2024 · In this paper, we propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL- ...
Sep 8, 2024 · The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug ...
Represents the drug-target binding affinity data as a hierarchical graph.•Hierarchically integrates coarse- and fine-level information in a coarse-to-fine ...
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity · 1 code implementation • 22 Mar 2022 • Zhaoyang Chu, Shichao ...
People also ask
What is drug-target binding affinity prediction?
What is the prediction of binding affinity?
A two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, is proposed, which delivers 1.5 percent CI ...