Research graph
References from Protein-ligand binding affinity prediction based on profiles of intermolecular contacts. Local targets link to admitted publications; unresolved targets remain external evidence.
Molecular docking and structure-based drug design strategies
10.3390/molecules200713384 · 2015 · External reference
10.3390/ijms20112783
10.3390/ijms20112783 · External reference
An overview of scoring functions used for protein–ligand interactions in molecular docking
10.1007/s12539-019-00327-w · 2019 · External reference
Structure-based drug design: docking and scoring
10.2174/138920307781369382 · 2007 · External reference
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening
2015 · External reference
10.1021/ci500731a
10.1021/ci500731a · External reference
10.1021/ci8001167
10.1021/ci8001167 · External reference
10.1023/a:1016357811882
10.1023/a:1016357811882 · External reference
Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?
10.1021/ci100343j · 2011 · External reference
Machine-learning scoring functions for structure-based drug lead optimization
2020 · External reference
K deep: protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks
10.1021/acs.jcim.7b00650 · 2018 · External reference
10.1093/bioinformatics/bty374
10.1093/bioinformatics/bty374 · External reference
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
10.1093/bioinformatics/btq112 · 2010 · External reference
10.1021/acsomega.9b01997
10.1021/acsomega.9b01997 · External reference
10.3389/fchem.2021.753002
10.3389/fchem.2021.753002 · External reference
The use of protein-ligand interaction fingerprints in docking
2008 · External reference
Apif: a new interaction fingerprint based on atom pairs and its application to virtual screening
10.1021/ci900043r · 2009 · External reference
Structural protein–ligand interaction fingerprints (splif) for structure-based virtual screening: method and benchmark study
10.1021/ci500319f · 2014 · External reference
10.1093/bioinformatics/bty757
10.1093/bioinformatics/bty757 · External reference
Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity
2021 · External reference
10.1021/jm048957q
10.1021/jm048957q · External reference
10.1093/bioinformatics/btu626
10.1093/bioinformatics/btu626 · External reference
Sfcscore rf: a random forest-based scoring function for improved affinity prediction of protein–ligand complexes
10.1021/ci400120b · 2013 · External reference
Improving autodock vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
10.1002/minf.201400132 · 2015 · External reference
The protein data bank
10.1093/nar/28.1.235 · 2000 · External reference
Binding affinity prediction for protein–ligand complexes based on βcontacts and b factor
10.1021/ci400450h · 2013 · External reference
Extended connectivity interaction features: Improving binding affinity prediction through chemical description
10.1093/bioinformatics/btaa982 · 2021 · External reference
Csar benchmark exercise of 2010: selection of the protein–ligand complexes
10.1021/ci200082t · 2011 · External reference
Encoding protein–ligand interaction patterns in fingerprints and graphs
10.1021/ci300566n · 2013 · External reference
10.1002/jcc.21334
10.1002/jcc.21334 · External reference
Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening
10.1371/journal.pone.0155183 · 2016 · External reference
