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Neural networks + Graph Theory: A new approach to protein folding(arxiv.org)

1202 points by proteinfolding 1 year ago flag hide 10 comments

johndoe 1 year ago next
This is really exciting! Combining neural networks and graph theory could be a game-changer for protein folding research. (https://arxiv.org/abs/XXXX)
- alice 1 year ago next
  @johndoe Agreed! Graph theory can provide a more intuitive understanding of protein structures. Do you think this method is scalable for larger proteins?
  johndoe 1 year ago next
  @alice It's a good question. The researchers indicated that their method could handle moderately large proteins, but further studies will determine its scalability. (https://proteinformatics.github.io/)
dr_data 1 year ago prev next
We've seen other interdisciplinary approaches using deep learning and graph theory in computer vision and recommendation systems. It's interesting to see this applied in bioinformatics. Any early benchmarks for this new method?
- ml_enthusiast 1 year ago next
  @dr_data The new approach showed significant improvements in protein folding speed and accuracy compared to previous methods. I can't post the link here, but you can find the benchmarks in the original research paper. (https://arxiv.org/pdf/XXXX.pdf)
code_monkey_83 1 year ago prev next
As a hobbyist in the biotech field, I'm curious about the implementation details. Does anyone know if the authors shared their source code for this method?
- johndoe 1 year ago next
  @code_monkey_83 Yes, the authors have shared their code on GitHub under the MIT license: https://github.com/ProteinFormatics/GraphNN
red_queen 1 year ago prev next
While this method is intriguing, we're still far from understanding protein function, right? Aren't there many other factors to consider besides folding?
- alice 1 year ago next
  @red_queen That's a valid point. Understanding protein function and interaction in vivo is a complicated problem. However, improved protein folding predictions could be an essential step towards better predicting function in particular contexts.
imoocchou 1 year ago prev next
It's amazing to see how interdisciplinary research has come so far. With the rapid advances in deep learning and graph theory, I wonder what other complex problems we might be able to tackle in the near future.

johndoe 1 year ago next
This is really exciting! Combining neural networks and graph theory could be a game-changer for protein folding research. (https://arxiv.org/abs/XXXX)
- alice 1 year ago next
  @johndoe Agreed! Graph theory can provide a more intuitive understanding of protein structures. Do you think this method is scalable for larger proteins?
  johndoe 1 year ago next
  @alice It's a good question. The researchers indicated that their method could handle moderately large proteins, but further studies will determine its scalability. (https://proteinformatics.github.io/)
dr_data 1 year ago prev next
We've seen other interdisciplinary approaches using deep learning and graph theory in computer vision and recommendation systems. It's interesting to see this applied in bioinformatics. Any early benchmarks for this new method?
- ml_enthusiast 1 year ago next
  @dr_data The new approach showed significant improvements in protein folding speed and accuracy compared to previous methods. I can't post the link here, but you can find the benchmarks in the original research paper. (https://arxiv.org/pdf/XXXX.pdf)
code_monkey_83 1 year ago prev next
As a hobbyist in the biotech field, I'm curious about the implementation details. Does anyone know if the authors shared their source code for this method?
- johndoe 1 year ago next
  @code_monkey_83 Yes, the authors have shared their code on GitHub under the MIT license: https://github.com/ProteinFormatics/GraphNN
red_queen 1 year ago prev next
While this method is intriguing, we're still far from understanding protein function, right? Aren't there many other factors to consider besides folding?
- alice 1 year ago next
  @red_queen That's a valid point. Understanding protein function and interaction in vivo is a complicated problem. However, improved protein folding predictions could be an essential step towards better predicting function in particular contexts.
imoocchou 1 year ago prev next
It's amazing to see how interdisciplinary research has come so far. With the rapid advances in deep learning and graph theory, I wonder what other complex problems we might be able to tackle in the near future.