EPISODE · Jan 15, 2019 · 12 MIN
DeepMind AlphaFold
from Siraj Raval
DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn't yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In this episode, i explain the different components of its architecture, why the protein folding problem is so important, and give some programmatic examples. Enjoy! Code for this video (with coding challenge): https://github.com/llSourcell/DeepFolding Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://deepmind.com/blog/alphafold/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://www.reddit.com/r/MachineLearning/comments/a2oaiy/r_alphafold_using_ai_for_scientific_discovery/ https://medium.com/syncedreview/deepmind-alphafold-delivers-unprecedented-progress-on-protein-folding-789fcc1420ea https://news.ycombinator.com/item?id=18587612 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
What this episode covers
DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn't yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In this episode, i explain the different components of its architecture, why the protein folding problem is so important, and give some programmatic examples. Enjoy! Code for this video (with coding challenge): https://github.com/llSourcell/DeepFolding Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://deepmind.com/blog/alphafold/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://www.reddit.com/r/MachineLearning/comments/a2oaiy/r_alphafold_using_ai_for_scientific_discovery/ https://medium.com/syncedreview/deepmind-alphafold-delivers-unprecedented-progress-on-protein-folding-789fcc1420ea https://news.ycombinator.com/item?id=18587612 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
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DeepMind AlphaFold
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