A deep-learning framework for multi-level peptide–protein interaction prediction
Peptide-protein interactions are involved in various fundamental cellular functions and their
identification is crucial for designing efficacious peptide therapeutics. Recently, a number of …
identification is crucial for designing efficacious peptide therapeutics. Recently, a number of …
A point cloud-based deep learning strategy for protein–ligand binding affinity prediction
Y Wang, S Wu, Y Duan, Y Huang - Briefings in Bioinformatics, 2022 - academic.oup.com
There is great interest to develop artificial intelligence-based protein–ligand binding affinity
models due to their immense applications in drug discovery. In this paper, PointNet and …
models due to their immense applications in drug discovery. In this paper, PointNet and …
Do deep learning models really outperform traditional approaches in molecular docking?
Molecular docking, given a ligand molecule and a ligand binding site (called``pocket'') on a
protein, predicting the binding mode of the protein-ligand complex, is a widely used …
protein, predicting the binding mode of the protein-ligand complex, is a widely used …
Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network
Proteins often interact with each other and form protein complexes to carry out various
biochemical activities. Knowledge of the interaction sites is helpful for understanding …
biochemical activities. Knowledge of the interaction sites is helpful for understanding …
Padme: A deep learning-based framework for drug-target interaction prediction
In silico drug-target interaction (DTI) prediction is an important and challenging problem in
biomedical research with a huge potential benefit to the pharmaceutical industry and …
biomedical research with a huge potential benefit to the pharmaceutical industry and …
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
Low-quality structural and interaction data improves binding affinity prediction via random forest
Docking scoring functions can be used to predict the strength of protein-ligand binding. It is
widely believed that training a scoring function with low-quality data is detrimental for its …
widely believed that training a scoring function with low-quality data is detrimental for its …
Predicting protein-ligand binding residues with deep convolutional neural networks
Y Cui, Q Dong, D Hong, X Wang - BMC bioinformatics, 2019 - Springer
Background Ligand-binding proteins play key roles in many biological processes.
Identification of protein-ligand binding residues is important in understanding the biological …
Identification of protein-ligand binding residues is important in understanding the biological …
Improving drug-target affinity prediction via feature fusion and knowledge distillation
Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug
discovery process. Recent studies show that deep learning models may have the potential …
discovery process. Recent studies show that deep learning models may have the potential …
[PDF][PDF] 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 …