NerLTR-DTA: drug–target binding affinity prediction based on neighbor relationship and learning to rank
Motivation Drug–target interaction prediction plays an important role in new drug discovery
and drug repurposing. Binding affinity indicates the strength of drug–target interactions …
and drug repurposing. Binding affinity indicates the strength of drug–target interactions …
graphDelta: MPNN scoring function for the affinity prediction of protein–ligand complexes
In this work, we present graph-convolutional neural networks for the prediction of binding
constants of protein–ligand complexes. We derived the model using multi task learning …
constants of protein–ligand complexes. We derived the model using multi task learning …
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
Motivation Sequence-based protein–protein interaction (PPI) prediction represents a
fundamental computational biology problem. To address this problem, extensive research …
fundamental computational biology problem. To address this problem, extensive research …
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
MM Stepniewska-Dziubinska, P Zielenkiewicz… - …, 2018 - academic.oup.com
Motivation Structure based ligand discovery is one of the most successful approaches for
augmenting the drug discovery process. Currently, there is a notable shift towards machine …
augmenting the drug discovery process. Currently, there is a notable shift towards machine …
Deep drug-target binding affinity prediction with multiple attention blocks
Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial
position in the drug discovery process. Many studies have introduced computational models …
position in the drug discovery process. Many studies have introduced computational models …
Deep graph learning of inter-protein contacts
Motivation Inter-protein (interfacial) contact prediction is very useful for in silico structural
characterization of protein–protein interactions. Although deep learning has been applied to …
characterization of protein–protein interactions. Although deep learning has been applied to …
Protein–protein contact prediction by geometric triangle-aware protein language models
Abstract Information regarding the residue–residue distance between interacting proteins is
important for modelling the structures of protein complexes, as well as being valuable for …
important for modelling the structures of protein complexes, as well as being valuable for …
DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model
Motivation Identifying the functional sites of a protein, such as the binding sites of proteins,
peptides, or other biological components, is crucial for understanding related biological …
peptides, or other biological components, is crucial for understanding related biological …
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …
BridgeDPI: a novel graph neural network for predicting drug–protein interactions
Motivation Exploring drug–protein interactions (DPIs) provides a rapid and precise approach
to assist in laboratory experiments for discovering new drugs. Network-based methods …
to assist in laboratory experiments for discovering new drugs. Network-based methods …