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Bio Bytes 23: Machine Learning and Structural Biology with Mohammed AlQuraishi

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Manage episode 311573213 series 3145074
Content provided by Columbia Sys Bio Initiative. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Columbia Sys Bio Initiative or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In which Vaibhav speaks with Dr. Mohammed AlQuraishi, an Assistant Professor of Systems Biology at the Columbia University Irving Medical Center, about using machine learning to predict protein structure. Among other things, they discuss the direction of algorithmic development in computational structure prediction, from neighborhood-based assembly of peptide fragments to modern applications of Deep Learning in structural modeling. They discuss the features of physical priors and discuss approaches in computationally optimizing protein-energy state predictions, taking into account the difficulties associated with the many local minima in an energy function. Throughout this discussion, Mohammed contextualizes the intuition behind the methods used by Deep Mind with their developments of AlphaFold.

  continue reading

37 episodes

Artwork
iconShare
 
Manage episode 311573213 series 3145074
Content provided by Columbia Sys Bio Initiative. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Columbia Sys Bio Initiative or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In which Vaibhav speaks with Dr. Mohammed AlQuraishi, an Assistant Professor of Systems Biology at the Columbia University Irving Medical Center, about using machine learning to predict protein structure. Among other things, they discuss the direction of algorithmic development in computational structure prediction, from neighborhood-based assembly of peptide fragments to modern applications of Deep Learning in structural modeling. They discuss the features of physical priors and discuss approaches in computationally optimizing protein-energy state predictions, taking into account the difficulties associated with the many local minima in an energy function. Throughout this discussion, Mohammed contextualizes the intuition behind the methods used by Deep Mind with their developments of AlphaFold.

  continue reading

37 episodes

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