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Portfolio to enable scalable delivery & dissemination of classroom based virtual drug discovery

This learning and teaching innovation project was funded by the Teaching Development Shape Fund in 2022/23



Project status

In progress


Project started on 1 Aug 2023

Portfolio package to enable scalable delivery and dissemination of classroom based virtual drug discovery exercise within Bath and beyond

Project Lead: Dr Stephen Flower, Department of Chemistry.

The project, which was funded by the Teaching Development Fund (TDF) Shape in 2022/23, The project will develop a package to aid the delivery of a virtual drug design exercise with team-based learning.

Our current open-ended, PGT team-based exercise is jointly run by the Departments of Chemistry and Life Sciences (additional input from Department of Health) using multiple online platforms and software packages to provide student-responsive data. It provides an unparalleled opportunity for students to experience the Hit-to-Lead pathway of the drug discovery process in a format far more engaging than traditional teaching methods.

In discussions, we realised that a casual PhD student could be employed (6 weeks) to code an API to automate these tasks, simplifying the teaching of the exercise, increasing its scalability, and producing a package that would ensure the exercise could be easily disseminated internationally. We have submitted a manuscript for publication detailing the core exercise to J. Chem. Ed. and already have interest from Dr Teresa Kaserer, University of Innsbruck (Austria) and Professor Antony Fairbanks, University of Canterbury (New Zealand).

Project evaluation will be through student trials, here and elsewhere. Dissemination will exploit EduFest, PedR and version-controlled updates of all related educational material within a National Teaching Repository Collection.

The project will simplify the production of data for students to analyse that is generated from their open-ended submissions. It will also reduce the workload put upon staff and reduce the time taken to produce the data, allowing staff to give the students higher quality feedback. This will allow the exercise to be extended, for deeper learning to occur, and for greater in-workshop discussion of the data.

Anticipated outcomes: a training and software package that will:

  • Generate physicochemical data
  • Allow uptake of the exercise by other institutions, nationally and internationally
  • Reduce reliance on a small number of experienced staff currently delivering the exercise, of which the loss of any one could stop the exercise from being delivered.
  • Academic publication
  • Publication of electronic resources

Dr Flower will report back on this project in summer 2024.