Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
Motivation Studies have shown that the accuracy of random forest (RF)-based scoring
functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of …
functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of …
Structure-based prediction of protein–peptide binding regions using Random Forest
Motivation Protein–peptide interactions are one of the most important biological interactions
and play crucial role in many diseases including cancer. Therefore, knowledge of these …
and play crucial role in many diseases including cancer. Therefore, knowledge of these …
Computationally predicting binding affinity in protein–ligand complexes: free energy-based simulations and machine learning-based scoring functions
Accurately predicting protein–ligand binding affinities can substantially facilitate the drug
discovery process, but it remains as a difficult problem. To tackle the challenge, many …
discovery process, but it remains as a difficult problem. To tackle the challenge, many …
LigScore: a novel scoring function for predicting binding affinities
A Krammer, PD Kirchhoff, X Jiang… - Journal of Molecular …, 2005 - Elsevier
We present two new empirical scoring functions, LigScore1 and LigScore2, that attempt to
accurately predict the binding affinity between ligand molecules and their protein receptors …
accurately predict the binding affinity between ligand molecules and their protein receptors …
Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment
Motivation: Identification of protein–ligand binding sites is critical to protein function
annotation and drug discovery. However, there is no method that could generate optimal …
annotation and drug discovery. However, there is no method that could generate optimal …
Statistical and machine learning approaches to predicting protein–ligand interactions
LJ Colwell - Current opinion in structural biology, 2018 - Elsevier
Data driven computational approaches to predicting protein–ligand binding are currently
achieving unprecedented levels of accuracy on held-out test datasets. Up until now …
achieving unprecedented levels of accuracy on held-out test datasets. Up until now …
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 …
Large-scale prediction of binding affinity in protein–small ligand complexes: The PRODIGY-LIG web server
Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the
prediction of binding affinity in protein–protein complexes. By using a combination of simple …
prediction of binding affinity in protein–protein complexes. By using a combination of simple …
[HTML][HTML] Improving peptide-protein docking with AlphaFold-Multimer using forced sampling
I Johansson-Åkhe, B Wallner - Frontiers in bioinformatics, 2022 - frontiersin.org
Protein interactions are key in vital biological processes. In many cases, particularly in
regulation, this interaction is between a protein and a shorter peptide fragment. Such …
regulation, this interaction is between a protein and a shorter peptide fragment. Such …
Pred-binding: large-scale protein–ligand binding affinity prediction
Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently,
experimental determination of compound–protein interactions remains challenging because …
experimental determination of compound–protein interactions remains challenging because …