Implementing a data driven pipeline for supramolecular drug design - challenges and FAIR practices
Thomas Allam 2 , Jennifer Hiscock 1 , Prof. Jeremy Frey 2 1 University of Kent, UK, 2 University of Southampton, UK
In academia, a large proportion of data sharing and analysis is conducted using spreadsheets. In this talk, we will discuss the advantages that come with the implementation of FAIR (Findable, Accessible, Interoperable, and Re-usable) databases to structure and share interdisciplinary siloed data from across multiple research groups. We will discuss our experience setting up and using data pipelines in academia and the challenges and benefits of doing so. We have attempted to implement data pipelines to store small and highly varied datasets of supramolecular molecules. In this poster we discuss how we have curated and structured the datasets, our attempt to model the small datasets to try to get insight into the mechanism of action of the compounds and our future plans for the work. References 1. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). 10.1038/sdata.2016.18 2. Hiscock, J. R et al, In situ modification of nanostructure configuration through the manipulation of hydrogen bonded amphiphile self-association, 10.1039/C6SM00529B 3. Allen et al, Towards the Prediction of Antimicrobial Efficacy for Hydrogen Bonded, SelfâAssociating Amphiphiles, 10.1002/ cmdc.202000533 4. Reller, L. B et al, Antimicrobial susceptibility testing: a review of general principles and contemporary practices, 10.1086/647952 5. Antimicrobial resistance : tackling a crisis for the health and wealth of nations / the Review on Antimicrobial Resistance chaired by Jim O'Neill
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