The Nobel Prize in Physics & Chemistry 2024:

The Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.

What are Artificial Neural Networks (ANNs)?

Artificial Neural Networks (ANNs) are a type of computing system inspired by the biological neural networks that constitute animal brains. ANNs are designed to mimic how human brains process information, learn from data, and make decisions.

They are a foundational technology behind many modern machine learning and artificial intelligence (AI) systems.

Key Concepts of Artificial Neural Networks:

1. Structure of ANNs:

Neurons: The basic units of ANNs are artificial neurons (also known as nodes or perceptrons), which simulate the behavior of biological neurons. These neurons receive inputs, process them, and produce an output.

Layers: ANNs are organized into layers of neurons:

Input layer: This layer receives raw data as input (e.g., images, text, or numerical data).

Hidden layers: These layers perform transformations on the input data through weights, biases, and activation functions. The network’s complexity and ability to learn complex patterns increase with more hidden layers.

Output layer: This layer produces the final output (e.g., a prediction, classification, or decision).

 2024 Nobel Prize in Chemistry:

• On 9th October 2024, David Baker, Demis Hassabis, and John Jumper were awarded the 2024 Nobel Prize in Chemistry for breakthroughs in computational protein design and protein structure prediction.

Key Points:

• David Baker’s Computational Protein Design: Baker was recognized for his pioneering work in computational methods that allow the design of new proteins, enabling the creation of designer proteins for specific applications.
• AlphaFold and Protein Structure Prediction; Hassabis and Jumper, creators of AlphaFold 2, revolutionized protein structure prediction by using AI to predict the structures of millions of proteins, solving a long-standing scientific challenge.
• Advancements in AI for Chemistry: The success of AlphaFold 2 in predicting complex protein structures represents a major step forward in biochemistry, transforming research into drug design, disease understanding, and molecular biology.
• Breakthroughs Recognized Soon After Discovery; Unlike many Nobel Prizes, which often come decades after the original research, this prize comes within just 4-6 years of the AlphaFold 2 development, reflecting its immediate global impact.
• Non-Chemists Honoured for Contributions: The award highlights the interdisciplinary nature of modern chemistry, with significant contributions from fields like artificial intelligence, extending chemistry’s influence into areas like biochemistry.

What is AlphaFold?

It is an artificial intelligence (AI) system developed by DeepMind, a subsidiary of Alphabet (Google’s parent company), to predict the 3D structure of proteins from their amino acid sequences. It represents a breakthrough in computational biology and protein folding, solving one of biology’s most challenging problems. Understanding the 3D structure of proteins is essential for various biological processes, including drug discovery, disease understanding, and bioengineering.

Types of AlphaFold Models:

1. AlphaFold 1:

Initial Model: The original version of AlphaFold competed in the 2018 CASP competition (CASP13) and performed remarkably well but was not the ultimate solution to the protein folding problem.

Focus: AlphaFold 1 was a combination of deep learning models and techniques based on graph neural networks, which allowed it to model protein folding pathways more efficiently than earlier methods.

2. AlphaFold 2:

1. • Breakthrough Version: Released in 2020, AlphaFold 2 marked a significant leap in the field. This version used an improved neural network architecture and more sophisticated models, allowing it to predict protein structures with almost experimental-level accuracy.
   • Transformers and Attention Mechanisms: AlphaFold 2 utilizes transformer networks, a type of neural network architecture that has been highly successful in natural language processing (NLP) tasks. By using “attention” mechanisms, the model can focus on different parts of the amino acid sequence when predicting folding patterns.
   • CASP14 Performance: AlphaFold 2 performed at an unprecedented level in CASP14, with some predictions coming very close to the experimentally-determined structures.