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Explore DeepMind's AlphaFold 2 Explained! AI Breakthrough in Protein Folding! What we know (& what we don't) with 242,564 views, 6,160 likes, and 367 comments. Experience the impact of this video content that has captured audience attention.

#deepmind #biology #ai This is Biology's AlexNet moment! DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. OUTLINE: 0:00 - Intro & Overview 3:10 - Proteins & Protein Folding 14:20 - AlphaFold 1 Overview 18:20 - Optimizing a differentiable geometric model at inference 25:40 - Learning the Spatial Graph Distance Matrix 31:20 - Multiple Sequence Alignment of Evolutionarily Similar Sequences 39:40 - Distance Matrix Output Results 43:45 - Guessing AlphaFold 2 (it's Transformers) 53:30 - Conclusion & Comments AlphaFold 2 Blog: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology AlphaFold 1 Blog: https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery AlphaFold 1 Paper: https://www.nature.com/articles/s41586-019-1923-7 MSA Reference: https://arxiv.org/abs/1211.1281 CASP14 Challenge: https://predictioncenter.org/casp14/index.cgi CASP14 Result Bar Chart: https://www.predictioncenter.org/casp14/zscores_final.cgi Paper Title: High Accuracy Protein Structure Prediction Using Deep Learning Abstract: Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the “protein folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFold has been recognised as a solution to this grand challenge by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP). This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world. Authors: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Kathryn Tunyasuvunakool, Olaf Ronneberger, Russ Bates, Augustin Žídek, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Martin Steinegger, Michalina Pacholska, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis. Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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