bindingdb — BindingDB protein-ligand binding affinities (2023-2026)

Description

Bipartite networks of small molecules (ligands) and the protein targets they bind, from BindingDB, the public database of measured binding affinities, mostly of drug-like molecules and drug targets. The measurements are curated by BindingDB from articles and US and WO patents, or imported from ChEMBL, PubChem BioAssay, the PDSP Ki database, and other sources (D3R, CSAR, the Taylor group at UCSD).

Nodes are ligands (BindingDB monomers) and targets (proteins, or complexes of several protein chains), and an edge connects a ligand to a target for each measurement of their binding (an inhibition constant $K_i$, a half-maximal inhibitory or effective concentration $IC_{50}$ or $EC_{50}$, a dissociation constant $K_d$, or association and dissociation rates), so that a ligand and a target can be connected by many edges (e.g. measurements in different articles or assays). Since the data do not give identifiers of the targets, targets are identified by the amino-acid sequences of their chains (the name of a target varies, e.g. with the range of residues of the construct, and the first one is given); the few targets without sequences are identified by their name and organism. The authors, institutions and publication dates of the measurements, the InChI strings and the links to other databases of the ligands, and the other properties of the target chains (PDB identifiers, names) are left out.

There is one network for each year from 2023 to 2026, from the quarterly archives of BindingDB in the UC San Diego Library Digital Collections (which start in October 2023), from the archives of October: 2023-10-01, 2024-10-01, 2025-10-01 and 2026-10-01.

The networks have the following properties:

(Node) is_ligand: whether the node is a ligand (otherwise it is a target).
(Node) name: the name of the ligand (often several synonyms separated by ::), or of the target.
(Node) monomer_id: the BindingDB identifier of the ligand (-1 for targets).
(Node) smiles, inchi_key: the SMILES string and the InChIKey of the ligand.
(Node) pubchem_cid, chebi_id, chembl_id, drugbank_id: the identifiers of the ligand in other databases.
(Node) organism: the organism of the target, as given by the curator or the data source.
(Node) num_chains: the number of protein chains of the target (more than one for complexes).
(Node) uniprot_ids: the UniProt (Swiss-Prot or, failing that, TrEMBL) accessions of the chains of the target.
(Node) chain_sequences: the amino-acid sequences of the chains of the target.
(Edge) reactant_set_id: the BindingDB identifier of the measurement.
(Edge) ki, ic50, kd, ec50 (nM), kon (1/(M s)), koff (1/s): the measured values (NaN when not given).
(Edge) qualifiers: the qualifiers of the measured values, when given as bounds (e.g. ic50 >, for $IC_{50} > 10000$ nM).
(Edge) ph, temp: the pH and the temperature (°C) of the measurement (NaN if not given).
(Edge) source: the source of the measurement (e.g. ChEMBL, US Patent, Curated from the literature by BindingDB).
(Edge) article_doi, pmid, pubchem_aid, patent_number: the article, PubChem assay or patent of the measurement.
(Edge) date (2025, 2026): the date at which the measurement was added to BindingDB (as Unix time; -1 if not given).

Tags

Biological Chemical Drug interactions Weighted Bipartite Multigraph Metadata

Added

2026-10-31

Citation

T. Liu, L. Hwang, S. K. Burley, C. I. Nitsche, C. Southan, W. P. Walters, M. K. Gilson, "BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data." Nucleic Acids Research 53, D1633-D1644 (2025) 10.1093/nar/gkae1075 Sci-Hub

Upstream URL OK

https://www.bindingdb.org/
https://library.ucsd.edu/dc/collection/bb03870458

Upstream license

Creative Commons Attribution 4.0 International (CC BY 4.0); data from ChEMBL: CC BY-SA 3.0, https://creativecommons.org/licenses/by/4.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
2023_10	1,197,976	2,772,869	4.63	100.96	179.96	8513.82	-0.31	21	0.98	Undirected	Bipartite	is_ligand name monomer_id smiles inchi_key pubchem_cid chebi_id chembl_id drugbank_id organism num_chains uniprot_ids chain_sequences	reactant_set_id ki ic50 kd ec50 kon koff ph temp qualifiers source article_doi pmid pubchem_aid patent_number	47.1 MiB	74.9 MiB	70.6 MiB	79.8 MiB
2024_10	1,280,021	2,923,143	4.57	102.61	180.83	8515.09	-0.30	20	0.97	Undirected	Bipartite	is_ligand name monomer_id smiles inchi_key pubchem_cid chebi_id chembl_id drugbank_id organism num_chains uniprot_ids chain_sequences	reactant_set_id ki ic50 kd ec50 kon koff ph temp qualifiers source article_doi pmid pubchem_aid patent_number	49.7 MiB	78.8 MiB	74.4 MiB	84.1 MiB
2025_10	1,362,774	3,078,835	4.52	103.59	181.48	5240.48	-0.30	20	0.97	Undirected	Bipartite	is_ligand name monomer_id smiles inchi_key pubchem_cid chebi_id chembl_id drugbank_id organism num_chains uniprot_ids chain_sequences	reactant_set_id ki ic50 kd ec50 kon koff ph temp qualifiers source article_doi pmid pubchem_aid patent_number date	52.9 MiB	83.9 MiB	79.3 MiB	89.4 MiB
2026_10	1,457,723	3,243,660	4.45	104.16	185.15	9911.36	-0.30	20	0.97	Undirected	Bipartite	is_ligand name monomer_id smiles inchi_key pubchem_cid chebi_id chembl_id drugbank_id organism num_chains uniprot_ids chain_sequences	reactant_set_id ki ic50 kd ec50 kon koff ph temp qualifiers source article_doi pmid pubchem_aid patent_number date	55.4 MiB	88.4 MiB	83.6 MiB	94.4 MiB