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

Shailima Rampogu1, Thananjeyan Balasubramaniyam2, Joon-Hwa Lee2

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|August 22, 2023
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Summary
This summary is machine-generated.

We developed the Curcumin Chalcone Derivatives Database (CCDD) for virtual screening of curcumin analogs. This webserver aids drug discovery researchers in identifying potential therapeutics and building new computational tools.

Keywords:
CADDChalconesCurcuminCurcumin chalcone derivatives database CCDDPythonStreamlit

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Curcumin and its analogs are promising therapeutic agents.
  • Effective virtual screening methods are crucial for identifying potent drug candidates.
  • Developing specialized databases can accelerate the drug discovery process.

Purpose of the Study:

  • To create a comprehensive database of curcumin and its analogs for virtual screening.
  • To develop a user-friendly webserver for analyzing compound properties.
  • To facilitate the identification of novel therapeutic agents.

Main Methods:

  • Utilized ChemBioOffice for 2D structure drawing and Discovery Studio Visualizer for 3D structure generation.
  • Developed the database using various Python modules for data handling and visualization.
  • Implemented Lipinski's rule of five for compound screening via a webserver interface.

Main Results:

  • The Curcumin Chalcone Derivatives Database (CCDD) was successfully constructed.
  • A webserver was developed, enabling virtual screening and compound property analysis.
  • Users can download compound structures (.sdf, .mol) and data frames (.csv).

Conclusions:

  • The CCDD webserver provides a valuable resource for computational drug discovery researchers.
  • This tool can expedite the identification of new therapeutics based on curcumin scaffolds.
  • The framework supports the development of novel webservers for drug discovery applications.