"Today we’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, 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." source: https://deepmind.com/blog/alphafold/

Over the past five decades, scientists have been able to determine shapes of proteins in labs using experimental techniques like cryo-electron microscopy, nuclear magnetic resonance or X-ray crystallography, but each method depends on a lot of trial and error, which can take years and cost tens of thousands of dollars per structure. This is why biologists are turning to AI methods as an alternative to this long and laborious process for difficult proteins.

Recently released by Deepmind, Alpha fold, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures in the CASP13 challenge. It outperformed all the other competitors and emerged first with a huge difference of correctly predicting 25 proteins correctly whereas the second place winner only predicted 9 of them correctly and that too with only 29K of the 129K present data about different proteins

This research is the greatest breakthrough in this field which will be able to predict how proteins fold for the formation of different types of proteins for different functions. This is important because this could lead to a better understanding and possibly a cure for diseases like Alzheimer's, mad cow's disease etc. because these diseases are believed to be caused due to malfunction in the folding of the proteins in the body.

The architecture for the network was simple, on a high level it constituted of residual convolutional neural network and gradient descent to optimize full protein features in the end.

The audience from this talk will be able to learn about how to reproduce the architecture of the Alpha Fold and also some basics about how different proteins strands affect the body and function of the proteins. This talk will be mostly on the technical side of the Alpha Fold.

 
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Outline/Structure of the Talk

On a very high level the presentation will be as follows:

  • Introduction to some concepts about different proteins (a very basic introduction)
  • What is the problem and what all this can solve.

  • The Problem
    • Different diseases caused by the problems

  • Previous implementation to solve these problems
    • Simple Deep Neural Network
      • An overview
      • The limitations
    • DeepSF
      • An overview
      • The limitations

  • the architecture of Alpha Fold
    • Some basic knowledge about the different algorithms used in its architecture
    • The complete architecture
    • Deep analysis on the different components

  • A basic implementation and Demo

Learning Outcome

The audience from this talk will be able to learn about how to reproduce the architecture of the Alpha Fold and also some basics about how different proteins strands affect the body and function of the proteins. This talk will be mostly on the technical side of the Alpha Fold.

Target Audience

Data Scientists, ML Enthusiasts and anyone who wants to learn about the Alpha Fold.

Prerequisites for Attendees

Some basic knowledge of Neural Networks and also some Knowledge of Machine Learning.

schedule Submitted 1 month ago

Public Feedback

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  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  3 weeks ago
    reply Reply

    Dear Aamir: If you had more time (say 90 minutes), could you do a full deep dive into protein folding, its challenges, how a neural network approach works for some small subset of the problems, with technical details, and working examples? 

    Warm Regards

    Vikas

    • Aamir Nazir
      By Aamir Nazir  ~  3 weeks ago
      reply Reply

      Greetings Dr Vikas,

      I would certainly be able to do a complete deep dive in 45-55 mins also, Thank You For the notice.

      Aamir Nazir


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