PDB-wide collection of binding data: current status of the PDBbind database

By Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, Renxiao Wang. 2015

607 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, methodology): “The authors reported signiﬁcant performance gains over existing methods on the PDBbind [37] benchmark dataset and highlighted the critical issues with regression-based frameworks [38,39].”
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “We used publicly available PDBbind v2020 dataset [4] which has the structures of 19443 protein-ligand complexes along with their experimentally measured binding affinity.”
Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction (2022, methodology): “For training and testing, we used the PDBbind database ([21, 22]), a curated set of 3D-structures of protein-ligand complexes and their corresponding experimentally determined binding affinity, obtained from the Protein Data Bank (PDB, [23]).”
Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration (2017, methodology): “We tested our theory on the core set of PDBbind 2015, which includes representative 195 protein-ligand complexes 15–17 .”
PubChem BioAssay: 2017 update (2016, methodology): “The database also contains literature-based data including both publication driven depositions submitted by journal authors and those contributed by PDBbind (10), IUPHAR (11), Bind-ingDB (12), ChEMBL (13) and other literature curation projects.”
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, background): “We further demonstrate that our unsupervised learned features could improve supervised afﬁnity prediction on PDBBind v2020 dataset (Liu et al., 2015).”
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, background): “1 3 taken from the PDBbind database, and both druggablity and site-identification function were tested [98].”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, methodology): “A similar trend was observed for structure-based modeling, for which databases such as PDBbind [91] and BindingDB [92] provide highly detailed structural information on protein-ligand complexes, as well as their associated bioactivity data.”