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

PeerJ. 2023 Aug 17:11:e15885. doi: 10.7717/peerj.15885. eCollection 2023.

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/.

Keywords: CADD; Chalcones; Curcumin; Curcumin chalcone derivatives database CCDD; Python; Streamlit.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chalcone*
  • Chalcones*
  • Curcumin*
  • Databases, Factual
  • Drug Discovery

Substances

  • Chalcones
  • Curcumin
  • Chalcone

Grants and funding

This work was supported by the National Research Foundation of Korea (2020R1A2C1006909 and 2022R1A4A1021817) and the Samsung Science and Technology Foundation (SSTF-BA1701-10). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.