Difference between revisions of "Protein Folding & Discovery"
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[http://www.google.com/search?q=AlphaFold+Deepmind+Architecture+Artificial+deep+machine+learning+ML ...Google search] | [http://www.google.com/search?q=AlphaFold+Deepmind+Architecture+Artificial+deep+machine+learning+ML ...Google search] | ||
| + | * [http://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery AlphaFold: Using AI for scientific discovery | A. Senior, J. Jumper, D. Hassabis, and P. Kohli] | ||
* [[Google DeepMind AlphaGo Zero]] | * [[Google DeepMind AlphaGo Zero]] | ||
* [[Google DeepMind AlphaStar]] | * [[Google DeepMind AlphaStar]] | ||
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Revision as of 00:03, 26 July 2020
Youtube search... ...Google search
- Case Studies
- COVID-19
- Service Capabilities
- Evolutionary Computation / Genetic Algorithms
- Architectures
- (Deep) Residual Network (DRN) - ResNet
Proteins are made up of hundreds or thousands of amino acids, and these amino acid sequences specify the protein's structure and function. But understanding just how to build these sequences to create novel proteins has been challenging. Past work has resulted in methods that can specify structure, but function has been more elusive. Machine learning reveals recipe for building artificial proteins | Emily Ayshford - University of Chicago - PhysOrg
Google DeepMind AlphaFold
Youtube search... ...Google search
- AlphaFold: Using AI for scientific discovery | A. Senior, J. Jumper, D. Hassabis, and P. Kohli
- Google DeepMind AlphaGo Zero
- Google DeepMind AlphaStar
- 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction
- Protein Data Bank | Wikipedia
DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence. AlphaFold: Using AI for scientific discovery | DeepMind
