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TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research.

Dominique SydowJaime Rodríguez-Guerra PedregalTalia B KimberDavid SchallerThorsten StumpfYonghui ChenMareike LejaSakshi MisraMichele WichmannArmin AriamajdAndrea Volkamer
Published in: Nucleic acids research (2022)
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, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that 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 automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.
Keyphrases
  • drug discovery
  • high throughput
  • quality improvement
  • healthcare
  • primary care
  • machine learning
  • working memory
  • deep learning
  • medical students
  • data analysis