In 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 Res. 2022 Jul 5;50(W1):W753-W760. doi: 10.1093/nar/gkac267.
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.
计算流程已成为现代药物发现活动的重要组成部分。然而，建立和维护此类流程可能具有挑战性并且耗时-特别是对于该领域的新手科学家而言。TeachOpenCADD 是一个旨在教授特定于领域的技能并提供流程模板作为研究项目起点的平台。我们以 Jupyter 笔记本的形式提供基于 Python 的解决方案，用于化学信息学和结构生物信息学中的常见任务，仅使用开源资源。包括新发布的 12 个补充内容，TeachOpenCADD 现在包含 22 个笔记本，涵盖理论背景和实践编程。为了促进可重复和可重用的研究，我们将软件最佳实践应用于笔记本，例如使用自动化持续集成进行测试，并遵循地道的 Python 风格。新的 TeachOpenCADD 网站可在 https://projects.volkamerlab.org/teachopencadd 上获得，并且所有代码都存储在 GitHub 上。
