Predicting protein-ligand binding structure using E (n) Equivariant graph neural networks
Drug design is a costly and time-consuming process, often taking more than 12 years and
costing up to billions of dollars. The COVID-19 pandemic has signified the urgent need for …
costing up to billions of dollars. The COVID-19 pandemic has signified the urgent need for …
VN-EGNN: E (3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Being able to identify regions within or around proteins, to which ligands can potentially
bind, is an essential step to develop new drugs. Binding site identification methods can now …
bind, is an essential step to develop new drugs. Binding site identification methods can now …
Predicting protein-ligand binding affinity with equivariant line graph network
Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for
drug repositioning and virtual drug screening. Existing approaches transform a 3D protein …
drug repositioning and virtual drug screening. Existing approaches transform a 3D protein …
Equipocket: an e (3)-equivariant geometric graph neural network for ligand binding site prediction
Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Most existing deep-learning methods consider a protein as a 3D image by spatially …
Most existing deep-learning methods consider a protein as a 3D image by spatially …
Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction
Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for
drug repositioning and virtual drug screening. Existing approaches usually transform a 3D …
drug repositioning and virtual drug screening. Existing approaches usually transform a 3D …
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Accurately predicting the binding affinity between proteins and ligands is crucial for drug
discovery. Recent advances in graph neural networks (GNNs) have made significant …
discovery. Recent advances in graph neural networks (GNNs) have made significant …
Edge-gated graph neural network for predicting protein-ligand binding affinities
Q Jiao, Z Qiu, Y Wang, C Chen… - 2021 IEEE international …, 2021 - ieeexplore.ieee.org
Predicting Protein-ligand binding affinities using Deep Learning can significantly shorten the
drug development cycle. Recently, Graph neural network models have been developed, and …
drug development cycle. Recently, Graph neural network models have been developed, and …
Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign)
Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Learning characteristics of graph neural networks predicting protein–ligand affinities
In drug design, compound potency prediction is a popular machine learning application.
Graph neural networks (GNNs) predict ligand affinity from graph representations of protein …
Graph neural networks (GNNs) predict ligand affinity from graph representations of protein …
Giant: Protein-ligand binding affinity prediction via geometry-aware interactive graph neural network
Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Recent advances have shown great promise in applying graph neural networks (GNNs) for …