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TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics
Dominique Sydow1, Jaime Rodríguez-Guerra1, Talia B Kimber1
1In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.
Nucleic Acids Research
|May 7, 2022
Summary
TeachOpenCADD provides accessible, open-source Python Jupyter notebooks for computational drug discovery pipelines. This platform aids novice scientists in learning cheminformatics and structural bioinformatics, promoting reproducible research.
Area of Science:
- Computational chemistry and structural biology
- Drug discovery and cheminformatics
Background:
- Computational pipelines are essential for drug discovery but complex for new researchers.
- Novice scientists face challenges in setting up and maintaining these pipelines.
Purpose of the Study:
- To provide an educational platform, TeachOpenCADD, for learning computational drug discovery skills.
- To offer reusable pipeline templates for research projects using open-source tools.
Main Methods:
- Development of Python-based Jupyter notebooks for common cheminformatics and structural bioinformatics tasks.
- Inclusion of theoretical background and hands-on programming exercises.
- Application of software best practices, including automated testing and idiomatic Python style.
Main Results:
- TeachOpenCADD now features 22 notebooks covering diverse topics in computational drug discovery.
- The platform utilizes only open-source resources, ensuring accessibility.
- Notebooks are designed for reproducibility and reusability, adhering to software engineering standards.
Conclusions:
- TeachOpenCADD effectively lowers the barrier to entry for computational drug discovery.
- The platform supports the development of reproducible and reusable research in the field.
- Accessible training and resources are crucial for advancing drug discovery research.
