Theoretical and Computational Chemistry

TeachOpenCADD 2021: Open Source and FAIR Python Pipelines to Assist in Structural Bioinformatics and Cheminformatics Research

Authors

  • Dominique Sydow Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Jaime Rodríguez-Guerra Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Talia B. Kimber Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • David Schaller Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Corey J. Taylor Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Yonghui Chen Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Mareike Leja Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Sakshi Misra Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Michele Wichmann Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Armin Ariamajd Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin ,
  • Andrea Volkamer Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin

Abstract

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming --- especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks and based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that each cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with an automated continuous integration and adhering to a more idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

Content

Thumbnail image of TeachOpenCADD_reloaded.pdf

Supplementary weblinks

TeachOpenCADD's official website
This website hosts a static view of TeachOpenCADD's Jupyter notebooks, allowing users to interact with the material in a read-only manner. Instructions on installation and contributions are also published here.
TeachOpenCADD's GitHub repository
This GitHub repository hosts all TeachOpenCADD-related code, incl. the Jupyter notebooks, the command-line interface, the continuous integration, the TeachOpenCADD website, and more.