Post by Daeyoung Lee

One of the most useful parts of entering drug discovery is seeing how the field is actually evolving in real time. #MilnerSymp26 gave me a much clearer picture of where parts of drug discovery are heading. Across the talks, there was a recurring theme of moving further upstream, towards the biological systems that drive disease, and building better ways to identify, model, and intervene on them. A few things that stood out: 1. Shift Bioscience’s work on linking cellular ageing to disease through perturbation modelling and virtual cell approaches. Using gene expression signatures and in silico models to identify rejuvenation targets is exactly the kind of work that makes ageing biology feel increasingly actionable rather than purely descriptive. 2. Immutrin’s antibody approach to ATTR amyloidosis, focusing not just on slowing further amyloid formation, but on depleting existing amyloid in the heart. It was a useful reminder that slowing disease progression and actually reversing pathology are very different therapeutic goals. 3. The broader emphasis on computational and platform-based discovery, from AI-driven target identification to early discussions around quantum approaches for molecular modelling, optimisation, and next-generation discovery workflows. For me, the biggest takeaway was how much therapeutic discovery is shifting toward understanding which biological processes are actually worth intervening on, and building better tools to make those decisions earlier and more intelligently. More than anything, it gave me a clearer picture of where I want to keep building my own understanding in computational biology and drug discovery. #DrugDiscovery #ComputationalBiology #Therapeutics #BiomedicalEngineering