Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …
based drug design. However, traditional machine learning (ML)-based methods based on …
Graph convolutional neural networks for predicting drug-target interactions
Accurate determination of target-ligand interactions is crucial in the drug discovery process.
In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting …
In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting …
Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level
H Cho, EK Lee, IS Choi - Scientific reports, 2020 - nature.com
Abstract Development of deep-learning models for intermolecular noncovalent (NC)
interactions between proteins and ligands has great potential in the chemical and …
interactions between proteins and ligands has great potential in the chemical 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 …
Predicting drug–target interaction using a novel graph neural network with 3D structure-embedded graph representation
We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …
graph neural network. We introduce a distance-aware graph attention algorithm to …
Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
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 …
GraphscoreDTA: optimized graph neural network for protein–ligand binding affinity prediction
Motivation Computational approaches for identifying the protein–ligand binding affinity can
greatly facilitate drug discovery and development. At present, many deep learning-based …
greatly facilitate drug discovery and development. At present, many deep learning-based …
Planet: a multi-objective graph neural network model for protein–ligand binding affinity prediction
X Zhang, H Gao, H Wang, Z Chen… - Journal of Chemical …, 2023 - ACS Publications
Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …
learning models have been published in recent years, where many of them rely on 3D …
Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
GW Kyro, RI Brent, VS Batista - Journal of Chemical Information …, 2023 - ACS Publications
Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
Extended connectivity interaction features: improving binding affinity prediction through chemical description
Motivation Machine-learning scoring functions (SFs) have been found to outperform
standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of …
standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of …