Knowledge Nugget of the day: Nobel Prize in Chemistry and Physics 2024

Subject: Science and Technology

Why in news?

The Nobel Prize in Chemistry 2024 has been awarded to David Baker, Demis Hassabis and John M Jumper. While Baker (62) won “for computational protein design”, the American Jumper (39) and Briton Hassabis (48) were honoured for “protein structure prediction”.

The Nobel Prize in Physics 2024 has been awarded to John Hopfield and Geoffrey Hinton. They have been awarded “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” Interestingly, both the Physics and Chemistry Nobel prizes this year have been given to artificial intelligence related research.

Key Takeaways :

Nobel Prize in Chemistry

1. Proteins are some of the most important life-sustaining molecules in any living organism. They perform a critical role in almost all biological processes. In fact, life itself would not be possible in absence of proteins. This is why these large and complex molecules have been subjects of scientific studies for decades.

2. This year’s Nobel Prize in Chemistry too was awarded for research on proteins. This award, however, is slightly different to previous ones. Scientists David Baker, Demis Hassabis and John Jumper have been honoured not for presenting any new insights into proteins themselves, but for developing tools that make it vastly easier and quicker to decipher their structures, and make entirely new proteins.

3. Hassabis and Jumper, who share one half of the prize, are co-creators of an artificial intelligence-based tool called AlphaFold2 that can predict the structure of a protein with outstanding accuracy. Baker used similar computational tools to create new proteins, which are not available in nature, but can perform many useful functions. Together, the trio managed to accomplish things that scientists have been striving to achieve for several decades.

4. The AlphaFold2 predicts the structures of proteins using known sequences of amino acids from the database. These predictions were then matched to catalogued protein structures in the other database. With training, the AI tool gained sufficient accuracy in predicting protein structures, given a particular sequence of amino acids.

Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins. (X/TheNobelPrize)

5. Baker relied on slightly different kinds of computations to design completely new, synthetic proteins that are not found in nature. The new proteins can perform functions that naturally-synthesised proteins are not designed to. Theoretically, for example, a synthetic protein can be designed to degrade plastics which are otherwise not biodegradable.

Learn through image: This year’s announcement marks the twenty-eighth occasion three laureates have shared the chemistry prize. (X/TheNobelPrize)

6. Significance: The work of the three scientists has huge implications in drug discovery, and in overcoming stubborn diseases that occur due to protein disorders.

Nobel Prize in Physics

1. The Nobel Prize in Physics 2024 has been awarded to John Hopfield and Geoffrey Hinton. While Hopfield, 91, is an American scientist and has been long respected in the field of biological physics, the British-Canadian Hinton, 76, has been called the ‘godfather of Artificial Intelligence’ (AI).

2. According to the Nobel Prize’s website, this year’s laureates used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning.

3. John Hopfield created a structure that can store and reconstruct information. Geoffrey Hinton invented a method that can independently discover properties in data and which has become important for the large artificial neural networks now in use.

4. The human brain broadly accumulates and processes information in three ways: through noticing the world around, through memory, and through the effort it puts in learning new things. The human brain can also think, using the stored information for various purposes.

5. As the Nobel website points out, today, machines can’t think, but can copy human functions of memory and learning. For example, a human brain, including that of a child, can look at an animal and tell it is a cat, even if it is a species of cat the child has never encountered before. Teaching a computer similar skills can mean that it can look at various pictures of human cells, and tell which cell is likely to be cancer-affected.

6. The big success of Hopfield and Hinton has been in developing computer algorithms that mimic the functioning of the human brain in performing common tasks. Hopfield built an artificial neural network, resembling the network of nerve cells in the human brain, that allowed computer systems to ‘remember’ and ‘learn’.

For example, when a Hopfield network is given new information, like an image or a song, it captures the entire pattern in one go, remembering the connections or relationships between the constituting parts, like pixels in the case of images. It allows the network to recall, identify, or regenerate that image or song when an incomplete, or similar-looking, image is passed as input.

Inspired by biological neurons in the brain, artificial neural networks are large collections of “neurons”, or nodes, connected by “synapses”, or weighted couplings, which are trained to perform certain tasks. (X/TheNobelPrize)

7. Hinton took forward the work of Hopfield and developed artificial networks that could perform much more complex tasks. So, while Hopfield networks could recognise simple patterns of shape or sound, Hinton’s advanced models could understand voices and pictures.

8. Hinton also developed a method called backpropagation that enabled the artificial neural networks to learn from previous mistakes and improve itself.

Geoffrey Hinton used a network developed by his co-laureate John Hopfield as the foundation for a new network: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. (X/TheNobelPrize)

9. Significance: Hopfield’s work was a leap towards enabling pattern recognition in computers, something that allows face recognition or image improvement tools that are common now.  Hinton demonstrated that deep networks resulted in the learning of more complex features and patterns in large datasets. Deep learning is at the heart of modern speech and image recognition, translation, voice assistance and self-driving cars.

BEYOND THE NUGGET: Protein- “Critical element of life”, 

1. Proteins are made of long chains of amino acids, which themselves are small organic molecules containing carbon, hydrogen, nitrogen and oxygen and sometimes sulphur. There are 20 different amino acids that serve as the building blocks of proteins.

2. Different combinations of amino acids, arranged in a sequence and folded tightly into unique three-dimensional shapes, form the proteins that are vital to almost all biological processes.

3. Certain kinds of proteins, called enzymes, can speed up biochemical reactions within the body, while others can provide structural support to cells and tissues. Then there are some proteins that help in immune response, while others can store nutrients or energy.

(Sources: AI to decipher, synthesise proteins(IE), Nobel Prize in Physics out (IE)

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