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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.
By Columbia Sys Bio Initiative5
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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.

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