AI-Powered AlphaFold 2: Revolutionizing Protein Folding

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SUMMARY

AlphaFold 2, developed by DeepMind, represents a significant advancement in protein folding prediction, having been trained on 100,000 proteins. This AI-powered tool achieves results comparable to experimental structures, with nearly two-thirds of its predictions aligning closely with actual data. John Moult, a computational biologist, emphasizes that the problem of protein folding is largely solved, marking a pivotal moment in biological research. However, the underlying mechanisms of how AlphaFold 2 operates remain complex and not fully understood, raising questions about the nature of artificial intelligence in scientific applications.

PREREQUISITES
  • Understanding of protein folding mechanisms
  • Familiarity with artificial neural networks (ANNs)
  • Knowledge of computational biology principles
  • Experience with machine learning algorithms
NEXT STEPS
  • Explore the technical details of AlphaFold 2's architecture and training methods
  • Research the implications of AI in computational biology
  • Study the CASP (Critical Assessment of protein Structure Prediction) benchmarks and results
  • Investigate the limitations and ethical considerations of AI in scientific research
USEFUL FOR

Researchers in computational biology, bioinformatics specialists, and professionals interested in the intersection of artificial intelligence and biological sciences will benefit from this discussion.

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Saying that humans aren't smart enough to predict the folding of a protein molecule is like saying that we don't have the arm strength to fly.

We concoct planes to fly and AI to solve the folding problem.
 
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.Scott said:
Saying that humans aren't smart enough to predict the folding of a protein molecule is like saying that we don't have the arm strength to fly.

We concoct planes to fly and AI to solve the folding problem.
Sure, but the difference is we understand how planes fly, but can't fully grasp the details on how & why these algorithms work for protein folding, Go, chess or whatever. You get a statistical certainty, but no absolute proof.
 
BWV said:
Sure, but the difference is we understand how planes fly, but can't fully grasp the details on how & why these algorithms work for protein folding...
Maybe we will understand when we fold proteins for as long as we've been flying airplanes.

On the other hand, we may bump into the conundrum of whether anything can totally understand itself, whether machine or animal.

Anyhow, food for endless thought, ...or nightmares!
 
If I use a computer to calculate pi to 1000 digits, is it because I am not smart enough to calculate it myself?
 
.Scott said:
If I use a computer to calculate pi to 1000 digits, is it because I am not smart enough to calculate it myself?
Not the same, as you know how to do the calculation and explicitly provide these instructions to the computer.

We don't really know how complex ANNs arrive at their answers - can certainly study and learn from them, but if we really understood AlphaZero, for example, then we could presumably create Stockfish or Deepblue type programs to beat it. protein folding is a larger combinatorial space than Go or Chess and unlike the games, there may be ‘rules‘. we are not aware of
 
More from Nature, this may be the biggest science story of the year
https://www.nature.com/articles/d41586-020-03348-4
This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.”
...

nearly two-thirds were comparable in quality to experimental structures. In some cases, says Moult, it was not clear whether the discrepancy between AlphaFold’s predictions and the experimental result was a prediction error or an artefact of the experiment.

1620846240680.jpeg
 
BWV said:
Not the same, as you know how to do the calculation and explicitly provide these instructions to the computer.
If I know the AI algorithm - and code it myself, then is it like long division?

Calling an algorithm "Artificial Intelligence" is like calling a driver-assist tool "Autopilot". The name alone tempts you to think of the application as independently smart and "person-like".
 
.Scott said:
Calling an algorithm "Artificial Intelligence" ...tempts you to think of the application as independently smart and "person-like".
I did not make up the term and am not tempted to think about ANNs as either ’smart’ or ‘person-like’
 
  • #10
BWV said:
I did not make up the term and am not tempted to think about ANNs as either ’smart’ or ‘person-like’
But coding up something that uses AI techniques is different than coding up something that doesn't rely on statistics or pattern discovery?
 
  • #11
.Scott said:
But coding up something that uses AI techniques is different than coding up something that doesn't rely on statistics or pattern discovery?
it is if you don't understand exactly the algorithm is doing. We don't understand exactly how or why AlphaZero is better at chess than Stockfish. One could imagine, as an analogy, some dumbass feeding a bunch of data into a NN of measurements of force, acceleration and mass then relying on the algorithm without understanding F=MA
 
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