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TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data.

Dominique Sydow1, Andrea Morger1, Maximilian Driller1

  • 1In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.

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|April 10, 2019
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Summary

TeachOpenCADD offers an open-source platform with interactive Jupyter notebooks to teach computer-aided drug design (CADD) concepts. This resource, developed by students, uses accessible cheminformatics and bioinformatics tools for new researchers.

Keywords:
CheminformaticsComputer-aided drug designLearningOpen sourcePythonRDKitStructural bioinformaticsTeaching

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Area of Science:

  • Computational chemistry and structural bioinformatics
  • Drug discovery and development

Background:

  • The rise of open-source software and data in cheminformatics and structural bioinformatics facilitates reproducible research in computer-aided drug design (CADD).
  • Existing documentation for CADD tools is available, but freely accessible, practical learning resources for beginners are scarce.

Purpose of the Study:

  • To present TeachOpenCADD, an educational platform designed to teach fundamental CADD concepts.
  • To provide interactive learning materials for students and new researchers in the field.

Main Methods:

  • Development of a teaching platform utilizing open-source compound and protein data.
  • Integration of basic and CADD-specific Python packages.
  • Creation of interactive Jupyter notebooks combining theoretical background with practical coding examples.

Main Results:

  • TeachOpenCADD provides a freely accessible, student-developed resource for learning CADD.
  • The platform includes interactive notebooks covering key CADD topics.
  • It leverages open-source tools and data for hands-on learning.

Conclusions:

  • TeachOpenCADD addresses the need for accessible educational materials in computer-aided drug design.
  • The platform empowers students and new researchers to engage with CADD principles through practical application.
  • It promotes reproducible and shareable CADD workflows.