Research graph
References from LGBind: A structure-guided deep learning framework for accurate prediction of protein-ligand binding sites. Local targets link to admitted publications; unresolved targets remain external evidence.
Data from: genome-scale annotation of protein binding sites via language model and geometric deep learning
2024 · External reference
The protein data bank
10.1093/nar/28.1.235 · 2000 · External reference
A deep learning approach to antibiotic discovery
10.1016/j.cell.2020.01.021 · 2020 · External reference
Foundations of biologically relevant artificial intelligence for drug discovery
2023 · External reference
RCSB protein data Bank: powerful new tools for exploring 3D structures of biological macromolecules
10.1093/nar/gkaa1038 · 2021 · External reference
ZINC20—A free ultralarge-scale chemical database for ligand discovery
10.1021/acs.jcim.0c00675 · 2020 · External reference
Deep learning for protein–ligand binding site prediction: a survey
2023 · External reference
IonCom: a sequence-based predictor for identifying metal ion binding sites in proteins
10.1093/bioinformatics/btw396 · 2016 · External reference
MIB: metal ion-binding site prediction and docking server
10.1021/acs.jcim.6b00407 · 2016 · External reference
GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithm and spatial sampling
2022 · External reference
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering
10.1109/tcbb.2013.104 · 2013 · External reference
Protein–ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data
10.1093/bioinformatics/btaa110 · 2020 · External reference
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues
10.1093/nar/gkab044 · 2021 · External reference
GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning
10.1093/nar/gkad288 · 2023 · External reference
Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning
10.1093/bib/bbac444 · 2022 · External reference
Genome-scale annotation of protein binding sites via language model and geometric deep learning
10.7554/elife.93695.3 · 2024 · External reference
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
10.1186/s13321-018-0285-8 · 2018 · External reference
DeepSurf: a surfacebased deep learning approach for the prediction of ligand binding sites on proteins
10.1093/bioinformatics/btab009 · 2021 · External reference
DeepPocket: ligand binding site detection and segmentation using 3D convolutional neural networks
10.1021/acs.jcim.1c00799 · 2022 · External reference
identifying binding residues for over 1000 ligands with relation-aware graph neural networks
10.1016/j.jmb.2023.168091 · 2023 · External reference
BioLiP2: an updated structure database for biologically relevant ligand–protein interactions
10.1093/nar/gkad630 · 2024 · External reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · 2023 · External reference
Unresolved reference
External reference
Large-scale chemical language representations capture molecular structure and properties
10.1038/s42256-022-00580-7 · 2022 · External reference
Learning from protein structure with geometric vector perceptrons
2021 · External reference
Metrics for 3D rotations: comparison and analysis
10.1007/s10851-009-0161-2 · 2009 · External reference
Unresolved reference
2017 · External reference
Unresolved reference
External reference
Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise
10.1021/ci300604z · 2013 · External reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · 2024 · External reference
Unresolved reference
2024 · External reference
Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2
10.1073/pnas.2212931120 · 2023 · External reference
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
10.1093/nar/gks966 · 2012 · External reference
CD-HIT suite: a web server for clustering and comparing biological sequences
10.1093/bioinformatics/btq003 · 2010 · External reference
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
10.1038/nbt.3988 · 2017 · External reference
A series of PDB related databases for everyday needs
10.1093/nar/gkq1105 · 2011 · External reference
ConvBERT: improving BERT with span-based dynamic convolution
2020 · External reference
Reduced surface: an efficient way to compute molecular surfaces
10.1002/(sici)1097-0282(199603)38:3<305::aid-bip4>3.0.co;2-y · 1996 · External reference
LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein
2025 · External reference
Mean shift: a robust approach toward feature space analysis
10.1109/34.1000236 · 2002 · External reference
Where metal ions bind in proteins
10.1073/pnas.87.15.5648 · 1990 · External reference
Inherent versus induced protein flexibility: comparisons within and between apo and holo structures
10.1371/journal.pcbi.1006705 · 2019 · External reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · 2021 · External reference
US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes
10.1038/s41592-022-01585-1 · 2022 · External reference
Nsp3 of coronaviruses: structures and functions of a large multi-domain protein
10.1016/j.antiviral.2017.11.001 · 2018 · External reference
Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2
10.1073/pnas.2212931120 · 2023 · External reference
Comparative assessment of scoring functions: the CASF-2016 update
10.1021/acs.jcim.8b00545 · 2019 · External reference
Visualizing data using t-SNE
2008 · External reference
PockFlex: a web server for flexibility-aware binding site identification and prioritisation from structural ensembles
10.1093/nar/gkag453 · 2026 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
External reference
Adam: a method for stochastic optimization
2015 · External reference
PyTorch: an imperative style, high-performance deep learning library
2019 · External reference
Reduced surface: an efficient way to compute molecular surfaces
10.1002/(sici)1097-0282(199603)38:3<305::aid-bip4>3.0.co;2-y · ExternalCitation · doi-reference
Metrics for 3D rotations: comparison and analysis
10.1007/s10851-009-0161-2 · ExternalCitation · doi-reference
Nsp3 of coronaviruses: structures and functions of a large multi-domain protein
10.1016/j.antiviral.2017.11.001 · ExternalCitation · doi-reference
A deep learning approach to antibiotic discovery
10.1016/j.cell.2020.01.021 · ExternalCitation · doi-reference
identifying binding residues for over 1000 ligands with relation-aware graph neural networks
10.1016/j.jmb.2023.168091 · ExternalCitation · doi-reference
ZINC20—A free ultralarge-scale chemical database for ligand discovery
10.1021/acs.jcim.0c00675 · ExternalCitation · doi-reference
DeepPocket: ligand binding site detection and segmentation using 3D convolutional neural networks
10.1021/acs.jcim.1c00799 · ExternalCitation · doi-reference
MIB: metal ion-binding site prediction and docking server
10.1021/acs.jcim.6b00407 · ExternalCitation · doi-reference
Comparative assessment of scoring functions: the CASF-2016 update
10.1021/acs.jcim.8b00545 · ExternalCitation · doi-reference
Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise
10.1021/ci300604z · ExternalCitation · doi-reference
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
10.1038/nbt.3988 · ExternalCitation · doi-reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · ExternalCitation · doi-reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · ExternalCitation · doi-reference
US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes
10.1038/s41592-022-01585-1 · ExternalCitation · doi-reference
Large-scale chemical language representations capture molecular structure and properties
10.1038/s42256-022-00580-7 · ExternalCitation · doi-reference
Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2
10.1073/pnas.2212931120 · ExternalCitation · doi-reference
Where metal ions bind in proteins
10.1073/pnas.87.15.5648 · ExternalCitation · doi-reference
Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning
10.1093/bib/bbac444 · ExternalCitation · doi-reference
Protein–ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data
10.1093/bioinformatics/btaa110 · ExternalCitation · doi-reference
DeepSurf: a surfacebased deep learning approach for the prediction of ligand binding sites on proteins
10.1093/bioinformatics/btab009 · ExternalCitation · doi-reference
CD-HIT suite: a web server for clustering and comparing biological sequences
10.1093/bioinformatics/btq003 · ExternalCitation · doi-reference
IonCom: a sequence-based predictor for identifying metal ion binding sites in proteins
10.1093/bioinformatics/btw396 · ExternalCitation · doi-reference
The protein data bank
10.1093/nar/28.1.235 · ExternalCitation · doi-reference
RCSB protein data Bank: powerful new tools for exploring 3D structures of biological macromolecules
10.1093/nar/gkaa1038 · ExternalCitation · doi-reference
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues
10.1093/nar/gkab044 · ExternalCitation · doi-reference
GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning
10.1093/nar/gkad288 · ExternalCitation · doi-reference
BioLiP2: an updated structure database for biologically relevant ligand–protein interactions
10.1093/nar/gkad630 · ExternalCitation · doi-reference
PockFlex: a web server for flexibility-aware binding site identification and prioritisation from structural ensembles
10.1093/nar/gkag453 · ExternalCitation · doi-reference
A series of PDB related databases for everyday needs
10.1093/nar/gkq1105 · ExternalCitation · doi-reference
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
10.1093/nar/gks966 · ExternalCitation · doi-reference
Mean shift: a robust approach toward feature space analysis
10.1109/34.1000236 · ExternalCitation · doi-reference
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering
10.1109/tcbb.2013.104 · ExternalCitation · doi-reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · ExternalCitation · doi-reference
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
10.1186/s13321-018-0285-8 · ExternalCitation · doi-reference
Inherent versus induced protein flexibility: comparisons within and between apo and holo structures
10.1371/journal.pcbi.1006705 · ExternalCitation · doi-reference
Genome-scale annotation of protein binding sites via language model and geometric deep learning
10.7554/elife.93695.3 · ExternalCitation · doi-reference
