Episode

Episode 14: The Drug Discovery Problems AI Alone Can't Solve

Podcast
From Models to Medicine
Published
Jun 24, 2026
Duration seconds
2747
Processing state
not_requested
Canonical source
https://podcasters.spotify.com/pod/show/kami-think-tank/episodes/Episode-14-The-Drug-Discovery-Problems-AI-Alone-Cant-Solve-e3k3eli
Audio
https://anchor.fm/s/110164688/podcast/play/120748146/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-4-30%2F1ce81024-6c58-a377-7673-a9f6bba3d663.mp3
JSON
/v1/public/podcasts/from-models-to-medicine-7769799/episodes/episode-14-the-drug-discovery-problems-ai-alone-can-t-solve
Markdown
/podcast/from-models-to-medicine-7769799/episode-14-the-drug-discovery-problems-ai-alone-can-t-solve.md

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Summary

In this episode of From Models to Medicine, we speak with Vid Stojevic , the co-founder and CEO of Kuano, a Cambridge-based company using quantum algorithms and AI to tackle the drug discovery problems that traditional computational chemistry keeps failing. In this episode, he explains why he deliberately ignored the broad platform play and went narrow instead, targeting the specific early-stage problems where getting the physics right changes everything. We get into what a "quantum lens" actually means in practice, why transition states are a better design target than natural substrates, and how Kuano is succeeding on targets that pharma had written off as undruggable. Vid makes a sharp case for how generating synthetic quantum data turns a low-data drug discovery problem into something AI can actually work with. He closes with honest advice on when quantum simulation is the right tool and when it simply isn't.