Evidence map›Paper›PMID 37605747›Full record

ArticlePeerJ2023

Curcumin Chalcone Derivatives Database (CCDD): a Python framework for natural compound derivatives database.

Shailima Rampogu, Thananjeyan Balasubramaniyam, Joon-Hwa Lee

Abstract read
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shailima Rampogu *Cachet Big Data Labs, Hyderabad, Telangana, India.
Thananjeyan Balasubramaniyam *Department of Chemistry, Gyeongsang National University, Jinju, Gyeongnam, South Korea.
Joon-Hwa LeeDepartment of Chemistry, Gyeongsang National University, Jinju, Gyeongnam, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We built the Curcumin Chalcone Derivatives Database (CCDD) to enable the effective virtual screening of highly potent curcumin and its analogs. The two-dimensional (2D) structures were drawn using the ChemBioOffice package and converted to 3D structures using Discovery Studio Visualizer V 2021 (DS). The database was built using different Python modules. For the 3D structures, different Python packages were used to obtain the data frame of compounds. This framework is also used to visualize the compounds. The webserver enables the users to screen the compounds according to Lipinski's rule of five. The structures can be downloaded in .sdf and .mol format. The data frame (df) can be downloaded in .csv format. Our webserver can help computational drug discovery researchers find new therapeutics and build new webservers. The CCDD is freely available at: https://srampogu-ccdd-ccdd-8uldk8.streamlit.app/.

Indexed as

ChalconeChalconesCurcuminDatabases, FactualDrug DiscoveryChalconeChalconesCurcuminCADDChalconesCurcuminCurcumin chalcone derivatives database CCDDPythonStreamlit

Identifiers

PMID37605747
PMCPMC10440061

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.