TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research. Issue Volume 50:Issue W1(2022) (7th May 2022)

• Record Type: Journal Article
• Authors: Sydow, Dominique; Rodríguez-Guerra, Jaime; Kimber, Talia B; Schaller, David; Taylor, Corey J; Chen, Yonghui; Leja, Mareike; Misra, Sakshi; Wichmann, Michele; Ariamajd, Armin; Volkamer, Andrea
• Is Part Of: Nucleic acids research. Volume 50:Issue W1(2022)
• Page Start: W753
• Page End: W760
• Publication Date: 2022-05-07
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac267
• Languages: English
• ISSNs: 0305-1048

Abstract

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, 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. Graphical Abstract: