Protein folding AI: "Will Change Everything"

In summary, Google's deep-learning program, AlphaFold2, has the potential to revolutionize biology by accurately determining the 3D shapes of proteins. This is an area where AI can greatly excel, as it is too complex for human brains to fully comprehend. The program uses a combination of physical principles and experimental results to predict protein structures, making it more effective than unassisted human minds. The recent success of AlphaFold2 in the Critical Assessment of Structure Prediction (CASP) challenge has been praised by researchers in the field.
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  • #2
Biology looks to be where AI can really shine, as it is simply too complex for human brains to grasp. Is the promise that AI can 'derive' biochemistry like a good human physicist can deduce a good hypothesis for physical phenomena from basic principles?
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As far as I understand it is definitely not deducing from basic principles, it is more like deducing any principles from experimental results. A very large number of them, and more than unassisted human minds manage to see the underlying patterns in. Er, I think that's induction rather than deduction.

Physical principles are used, seemingly to a limited extent by the programme, according to this related article - the programme runs through structures to eliminate physically impossible ones. I would guess the combination of approaches, using physical calculations to refine the inductively predicted structures will in future be very powerful.

1. What is protein folding AI?

Protein folding AI is a type of artificial intelligence technology that uses algorithms and machine learning to predict the 3D structure of proteins. This is important because the shape of a protein determines its function, and understanding protein structure can lead to breakthroughs in medicine and bioengineering.

2. How does protein folding AI work?

Protein folding AI works by analyzing large amounts of data on protein sequences and structures. The AI uses this data to make predictions about how a specific protein sequence will fold into a 3D structure. These predictions are then compared to experimental data and refined over time, allowing the AI to improve its accuracy.

3. What are the potential applications of protein folding AI?

Protein folding AI has the potential to revolutionize drug discovery and development by helping researchers better understand how proteins interact with potential drug compounds. It can also aid in the design of new proteins for various purposes, such as enzyme production and biomaterials.

4. How accurate is protein folding AI?

The accuracy of protein folding AI varies depending on the specific algorithm and dataset used. However, recent advancements in deep learning have significantly improved the accuracy of protein folding predictions, with some algorithms achieving accuracy levels of over 90%.

5. What are the ethical concerns surrounding protein folding AI?

One ethical concern is the potential impact on human labor, as protein folding AI could potentially replace jobs in protein structure prediction. There are also concerns about data privacy and ownership, as large databases of protein sequences and structures are needed to train the AI. Additionally, there may be ethical considerations around the use of AI in drug development and potential biases in the data used to train the algorithms.

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