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How Google DeepMind's AlphaFold uses artificial intelligence to predict protein structures, transforming biological research and drug discovery.
The biggest problems in the world might be solved by tiny molecules unlocked using AI. Take your big idea online today with ve42.co/hostinger - code VE at checkout. A huge thank you to John Jumper and Kathryn Tunyasuvunakool at Google Deepmind; and to David Baker and the Institute for Protein Design at the University of Washington for their invaluable expertise and explanations. We’re incredibly grateful to Juanita Bagawan and Lucie Kerley at Google Deepmind, and Ian C. Haydon at the UW Institute for Protein Design for their assistance with some of the animation and imagery used in this video. Special thanks to those at Google DeepMind Production Studios for their help interviewing John Jumper: Director - Bernardo Resende Director of Photography - Robert Messere Senior Audio Technician - Perry Rogantin Senior Production Coordinator - Sarah Ellen Morton Studio Manager - Nicholas Duke For more on this topic head over to our Patreon for the exclusive extended interview patreon.com/posts/patreon-with-cut-122274530?utm_medium=clip… ▀▀▀ How to determine protein structures Why are proteins so complicated? The CASP Competition and Deep Mind How does Alphafold work? 3 ways to get better AI What is a Transformer in AI? The Structure Module Alphafold 2 wins the Nobel Prize Designing New Proteins - RF Diffusion The Future of AI ▀▀▀ Try Snatoms! A molecular modelling kit I invented where the atoms snap together. ve42.co/SnatomsV Sign up to the Veritasium newsletter for weekly science updates - ve42.co/Newsletter ▀▀▀ References and Credits: How AI Revolutionized Protein Science, but Didn’t End It. (Jun 2024) via Quanta Magazine - ve42.co/airev Jumper, J., Evans, R., Pritzel, A. et al. (Jul 2021). Highly accurate protein structure prediction with AlphaFold. Nature - ve42.co/alphafoldtwo Senior, A.W., Evans, R., Jumper, J. et al. (Jan 2020). Improved protein structure prediction using potentials from deep learning. Nature - ve42.co/alphafold R Zwanzig, A Szabo, B Bagchi (Jan 1992). Levinthal's paradox. PMC, National Institute of Health - ve42.co/levparadox Khatib, F., DiMaio, F., Foldit Contenders Group. et al. (Sept 2011). Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nature - ve42.co/proteinfoldingsolved DeepMind co-founder: Gaming inspired AI breakthrough. (Dec 2020) via BBC - ve42.co/aibreakthrough 3Blue1Brown. (Apr 2024). Transformers (how LLMs work) explained visually | DL5 via Youtube - ve42.co/transformers AlphaFold impact stories. via Deepmind Blog - ve42.co/foldimpact Quanta Magazine. (Oct 2024). How AI Cracked the Protein Folding Code and Won a Nobel Prize via Youtube - ve42.co/proteinfolding Looking Glass Universe. (Apr 2024). how AlphaFWhat people say. Recommended for its clear explanation of how AI-driven protein folding predictions are being used to design synthetic antibodies and solve biological challenges.
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