BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction
Motivation The identification of compound–protein interactions (CPIs) is an essential step in
the process of drug discovery. The experimental determination of CPIs is known for a large …
the process of drug discovery. The experimental determination of CPIs is known for a large …
Perceiver CPI: a nested cross-attention network for compound–protein interaction prediction
Motivation Compound–protein interaction (CPI) plays an essential role in drug discovery
and is performed via expensive molecular docking simulations. Many artificial intelligence …
and is performed via expensive molecular docking simulations. Many artificial intelligence …
MONN: a multi-objective neural network for predicting compound-protein interactions and affinities
Computational approaches for understanding compound-protein interactions (CPIs) can
greatly facilitate drug development. Recently, a number of deep-learning-based methods …
greatly facilitate drug development. Recently, a number of deep-learning-based methods …
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 …
Boosting compound-protein interaction prediction by deep learning
The identification of interactions between compounds and proteins plays an important role in
network pharmacology and drug discovery. However, experimentally identifying compound …
network pharmacology and drug discovery. However, experimentally identifying compound …
MGPLI: exploring multigranular representations for protein–ligand interaction prediction
Motivation The capability to predict the potential drug binding affinity against a protein target
has always been a fundamental challenge in silico drug discovery. The traditional …
has always been a fundamental challenge in silico drug discovery. The traditional …
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …
and chemogenomics studies, and proteins without three-dimensional structure account for a …
Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
Background Accurate prediction of protein–ligand binding affinity is important for lowering
the overall cost of drug discovery in structure-based drug design. For accurate predictions …
the overall cost of drug discovery in structure-based drug design. For accurate predictions …
DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks
Motivation An essential part of drug discovery is the accurate prediction of the binding affinity
of new compound–protein pairs. Most of the standard computational methods assume that …
of new compound–protein pairs. Most of the standard computational methods assume that …
SE-OnionNet: a convolution neural network for protein–ligand binding affinity prediction
S Wang, D Liu, M Ding, Z Du, Y Zhong, T Song… - Frontiers in …, 2021 - frontiersin.org
Deep learning methods, which can predict the binding affinity of a drug–target protein
interaction, reduce the time and cost of drug discovery. In this study, we propose a novel …
interaction, reduce the time and cost of drug discovery. In this study, we propose a novel …