Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 223

The DOI matches its Crossref record.

BindingDB: A Web-Accessible Molecular Recognition Database

Chen, Xi, Liu, Ming, Gilson, Michael·2001·Combinatorial Chemistry & High Throughput Screening

Abstract

This paper presents an initial description of the BindingDB, a public web-accessible database of measured binding affinities for various molecular types (http://www.bindingdb.org). The BindingDB allows queries based upon a range of criteria, including chemical similarity or substructure, sequence homology, numerical criteria (e.g. delta G(o) < 5 kcal/mol) and reactant names (e.g. "lysozyme"). Principles of Human-Computer Interactions are being employed in creating the query interface and user-feedback is being solicited. The data specification includes significant experimental detail. A full dictionary has been created for isothermal titration calorimetry data in consultation with experimentalists and data dictionaries for enzyme-inhibition and other measurement techniques are being developed. Currently, the BindingDB contains several data sets of broad interest, such as antigen-antibody binding and cyclodextrin/small molecule binding. However, it is anticipated that online deposition by experimentalists will ultimately contribute to a larger flow of data. We are actively developing software and file specifications to facilitate such deposition.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2174/1386207013330670

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 223

Metadata

Authors
Chen, X., Liu, M., & Gilson, M.
Venue
Combinatorial Chemistry & High Throughput Screening
Published
2001-12-01
Volume
4
Issue
8
Pages
719-725
ISSN
1386-2073 (ISSN-L)
OpenAlex
W2111078532
Cited by
223
References
0
FWCI
3.46
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: BindingDB: A Web-Accessible Molecular Recognition Database. https://app.cochat.ai/papers/10.2174/1386207013330670
The paper
Chen, X., Liu, M., & Gilson, M. (2001). BindingDB: A Web-Accessible Molecular Recognition Database. Combinatorial Chemistry & High Throughput Screening. https://doi.org/10.2174/1386207013330670

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.