Deep learning with feature embedding for compound-protein interaction prediction
F Wan, J Zeng - Biorxiv, 2016 - biorxiv.org
Accurately identifying compound-protein interactions in silico can deepen our understanding
of the mechanisms of drug action and significantly facilitate the drug discovery and …
of the mechanisms of drug action and significantly facilitate the drug discovery and …
[HTML][HTML] Prediction of chemical-protein interactions network with weighted network-based inference method
Chemical-protein interaction (CPI) is the central topic of target identification and drug
discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo …
discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo …
[HTML][HTML] PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Background Predicting protein-ligand binding sites is a fundamental step in understanding
the functional characteristics of proteins, which plays a vital role in elucidating different …
the functional characteristics of proteins, which plays a vital role in elucidating different …
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 …
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 …
Binding Affinity Prediction for Protein–Ligand Complexes Based on β Contacts and B Factor
Accurate determination of protein–ligand binding affinity is a fundamental problem in
biochemistry useful for many applications including drug design and protein–ligand docking …
biochemistry useful for many applications including drug design and protein–ligand docking …
[HTML][HTML] The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
Abstract Structure-based drug design depends on the detailed knowledge of the three-
dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of …
dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of …
AttentionDTA: prediction of drug–target binding affinity using attention model
In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an
important role in virtual screening of drug discovery. DTI prediction, which have been treated …
important role in virtual screening of drug discovery. DTI prediction, which have been treated …
[HTML][HTML] Plas-5k: Dataset of protein-ligand affinities from molecular dynamics for machine learning applications
Computational methods and recently modern machine learning methods have played a key
role in structure-based drug design. Though several benchmarking datasets are available …
role in structure-based drug design. Though several benchmarking datasets are available …
PRODIGY: a contact-based predictor of binding affinity in protein-protein complexes
A Vangone, AMJJ Bonvin - Bio-protocol, 2017 - bio-protocol.org
Biomolecular interactions between proteins regulate and control almost every biological
process in the cell. Understanding these interactions is therefore a crucial step in the …
process in the cell. Understanding these interactions is therefore a crucial step in the …