Category: Drug Discovery

  • MHRA launches AI sandbox to accelerate medicines development and improve safety

    New AI sandbox will help make medicines safer, speed up development, and reduce reliance on animal testing.

    If you are involved in using AI/ML in drug discovery then this initiative could well be of interest.

    The UK will launch a first-of-its-kind initiative to test how artificial intelligence (AI) can help make medicines safer for patients – as announced by the Science Minister Lord Vallance during London Tech Week today (9 June 2026). 

    The programme will explore how AI can improve the assessment of accuracy and safety, better predict risks, and detect effects that existing approaches may not capture.  

    More details are here https://www.gov.uk/government/news/mhra-launches-ai-sandbox-to-accelerate-medicines-development-and-improve-safety

  • Kiin Bio free offer

    As part of my work I’ll be invited to help startups or review spinouts, this is always really interesting to learn about new science or insights from really smart scientists. However, one of the problems is often navigating through unlabelled presentations or disparate folders on different computers containing excel, word, pdfs etc. Simply putting everything in a data room to let 3rd parties try to navigate is not a viable solution.

    So I’m always interested in potential solutions, Rachel Skyner (who I first met when she worked on Fragalysis) highlighted an interesting looking programme. Kiin are offering elected academic and nonprofit teams get one year of free access to the Kiin Pioneer Programme and access to their drug discovery platform and hands-on support from the science team.

    Research teams are generating more data than ever before, but scientific discovery often stalls at the point of hypothesis-generation and decision-making.

    Promising early findings are often spread across papers, datasets, internal notes, and expert judgment. Priorities can be hard to compare, hypotheses difficult to track, and promising signals slow to translate into action.

    This programme is designed for teams who want to make their discovery process faster, more systematic, transparent, and actionable.

    We are especially interested in teams working on questions such as:

    • Which targets should we prioritise, and why?
    • Which hypotheses are worth testing next?
    • How should we interpret conflicting evidence across datasets?
    • Where are the strongest translational opportunities?
    • How can we make complex scientific decisions easier to track, explain, and revisit?

    No cost, no data transfer, all IP stays with your institution, available to academics and non-profits. Applications close August.