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Study mode:
on
1
intro
2
preamble
3
who am i
4
agenda
5
proteins
6
why do we need proteins?
7
msa
8
structure prediction methods
9
three-body problem
10
molecular dynamics
11
homology modeling
12
alphafold 1
13
alphafold 2
14
single seq
15
alphafold 3
16
conclusion
17
questions?
Description:
Explore the cutting-edge field of protein structure prediction through deep learning in this conference talk by Iaroslav Geraskin at Conf42 ML 2024. Delve into the fundamental concepts of proteins and their importance before examining various structure prediction methods, including molecular dynamics and homology modeling. Gain insights into the evolution of AlphaFold, from its initial version to AlphaFold 3, and understand how deep learning techniques are revolutionizing this critical area of computational biology. Learn about the challenges faced in protein structure prediction, such as the three-body problem, and discover how advanced AI models are overcoming these obstacles. Conclude with a comprehensive overview of the current state and future prospects of deep learning applications in protein structure prediction.

Deep Learning for Protein Structure Prediction

Conf42
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