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UID:pretalx-europython-2026-VNR377@programme.europython.eu
DTSTART;TZID=CET:20260716T141500
DTEND;TZID=CET:20260716T144500
DESCRIPTION:Developing a single drug takes 12 years with only a 12% chance 
 of success. AI is changing this dramatically: the first AI designed drug h
 ave reached human trials\, Alphafold won a Nobel Prize and pharmaceutical 
 companies have committed billions to AI partnerships. The best part? The P
 ython ecosystem you already know is powering this revolution. This talk in
 troduces AI drug discovery to Python developers with no biology or chemist
 ry background required. We'll start with the key insight that makes this f
 ield accessible: molecules are data structures\, proteins are strings\, an
 d drug target binding is just API matching. Through a demo\, attendees wil
 l see how to represent and visualize molecules with RDKit\, convert chemic
 al structures into ML ready vectors\, predict drug properties like toxicit
 y and solubility using graph neural networks with DeepChem and predict 3D 
 protein structures in seconds using the ESMFold API.
DTSTAMP:20260524T130740Z
LOCATION:Chamber Hall A (S3A)
SUMMARY:From Molecules to Models: A Guide to AI Drug Discovery with Python 
 - Jenny Vega
URL:https://programme.europython.eu/europython-2026/talk/VNR377/
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