From computational possibility to testable hypotheses.

AI Drug Discovery

A computational research programme exploring drug repurposing and candidate prioritisation, with experimental validation undertaken through appropriately qualified research partners.

This programme forms part of the unit’s launch agenda. Each project proceeds through defined scientific, resourcing, data-access and governance gates before research execution.

A defined question. A transparent method.

Select a defined therapeutic area and a research question with a realistic validation pathway.

Assess suitable public or licensed datasets, provenance, quality and permitted uses.

Evaluate computational methods against appropriate baselines and independent test data.

Prioritise hypotheses for expert review and prospective experimental validation.

Drug-repurposing feasibility study

Can a reproducible computational workflow identify a small, defensible set of repurposing hypotheses in one selected therapeutic area?

Evidence review, dataset assessment, baseline comparison, documented model evaluation and specialist review. Therapeutic-area selection and the final protocol are the first research milestones.

State what the evidence can support.

A computational prediction is not a validated medicine, a clinical recommendation or evidence of treatment efficacy. Computational findings will be reported separately from experimental validation and any clinical development.

Each project should define oversight, data permissions, any required approvals, analysis methods, validation needs and publication arrangements. Completed results and confirmed partnerships will be identified in the unit’s publication and collaboration records.