34:I[58016,["/_next/static/chunks/a10b49cc1be72925.js","/_next/static/chunks/b5ea50e123aa84a6.js","/_next/static/chunks/48003610e91209eb.js","/_next/static/chunks/de880ec5a0241b38.js","/_next/static/chunks/81ce7bf5f20dbd5a.js","/_next/static/chunks/49260bad4a6c1c3e.js","/_next/static/chunks/53f751967ccc7d59.js","/_next/static/chunks/15a1b1fbd3cf7d4a.js","/_next/static/chunks/9d18ca3107644c0d.js","/_next/static/chunks/88bed98e5cd5e639.js","/_next/static/chunks/2a1278cba709ea3b.js"],"ProcessEpisodeButton"] 38:I[56939,["/_next/static/chunks/a10b49cc1be72925.js","/_next/static/chunks/b5ea50e123aa84a6.js","/_next/static/chunks/48003610e91209eb.js","/_next/static/chunks/de880ec5a0241b38.js","/_next/static/chunks/81ce7bf5f20dbd5a.js","/_next/static/chunks/49260bad4a6c1c3e.js","/_next/static/chunks/53f751967ccc7d59.js","/_next/static/chunks/15a1b1fbd3cf7d4a.js","/_next/static/chunks/9d18ca3107644c0d.js","/_next/static/chunks/88bed98e5cd5e639.js","/_next/static/chunks/2a1278cba709ea3b.js"],"EpisodeTranscript"] 2e:["$","div",null,{"className":"flex flex-col gap-2 text-sm text-muted-foreground","children":[["$","span",null,{"className":"inline-flex items-center gap-2 bg-secondary/50 px-3 py-1 rounded-full","children":[["$","svg",null,{"className":"w-4 h-4","fill":"none","stroke":"currentColor","viewBox":"0 0 24 24","children":["$","path",null,{"strokeLinecap":"round","strokeLinejoin":"round","strokeWidth":2,"d":"M8 7V3m8 4V3m-9 8h10M5 21h14a2 2 0 002-2V7a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z"}]}],["$","time",null,{"dateTime":"2026-01-21T14:00:00.000Z","children":"Wed Jan 21 2026"}]]}],null]}] 35:T4f2,

For Dr. Marinka Zitnik, the promise of AI in medicine begins with acknowledging the scale of the problem. Most patients with rare diseases have no approved treatments, and traditional drug development timelines make progress painfully slow. In this conversation, she describes how AI-driven drug repurposing offers a way to work within existing constraints while still opening new therapeutic possibilities.

She also highlights a structural issue that has limited impact: machine learning and biology communities often work in parallel, not together. By building shared benchmarks and collaborative spaces, Marinka argues, researchers can focus models on problems that truly matter for patients.

The episode introduces her definition of AI agents as systems that can take actions and learn from outcomes — a capability she sees as essential for scientific discovery beyond static prediction. Throughout the discussion, Marinka returns to the value of academic freedom: the ability to chase difficult questions that require long time horizons and interdisciplinary thinking.

Transcript.

Bridging AI and Biology to Tackle Medicine’s Hardest Problems with Dr. Marinka Zitnik

Powered by Wave AI