Google DeepMind CEO and co-founder Demis Hassabis has again made headlines as he received this year’s Noble Prize for Chemistry for his revolutionary work in AplhaFold, which is an artificial intelligence (AI) system that can make predictions of the 3-D structure of proteins. This achievement is a significant milestone not only for biological science but also for AI.
What’s New:
Hassabis along with John Jumper and David Baker were awarded this year’s Nobel Prize for their contributions to protein science on October 9, 2024. Alphafold has revolutionised how scientists understand proteins by accurately predicting their structures by the sequence of amino acids. The technology by Alphafold is available freely for researchers worldwide, enhancing the work of more than 2 million scientists to advance their work in various fields.
Key Insight:
Before the AlphaFold prediction of a protein’s structure was a complex and time-consuming task that sometimes required years of research. Hassabis recognised this challenge and developed AI that could be capable of solving it. Today AlphaFold can predict every known protein which demonstrates the potential of AI to tackle some of the most crucial problems in science.
“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.
How This Works:
Baker has worked on creating new proteins using computational methods. His team has successfully designed proteins that do not exist in nature and that can be used in making medicines and vaccines.
Meanwhile, AlphaFold uses advanced Machine Learning to analyse the vast amount of data about all the known protein structures, which helps it to predict the structure of proteins. Further, it learns and improves its vast amount of data over time, which increases its accuracy over time. The system not only predicts the 3D structure of proteins but also provides reliability scores for each part which ensures that researchers can trust its findings.
Results:
The impact of AlphaFold has been profound. It has predicted nearly every known protein structure and that is around 200 million in total and has been cited in thousands of research papers since its release. Its predictions have accelerated drug discovery processes and enhanced our understanding of fundamental biological mechanisms. Hassabis’s vision for AlphaFold is not just to solve existing problems but to open new avenues for research and discovery.
Why This Matters:
Hassabis believes that AI can significantly improve human lives by solving complex scientific problems. The implications of AlphaFold extend beyond basic biology; it holds promise for developing new medications and treatments for diseases. As more scientists are increasingly adopting AI technologies we can expect a new era of accelerated scientific inventions.
We’re Thinking:
His commitment to using AI for the greater good of humanity highlights the catalytic potential of technology in advancing human knowledge. As he stated after receiving the Nobel Prize, “I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people. AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”
Hassbis has shown with AlphaFold how AI can be used to enhance other fields of science. He sees a future where AI will continue to evolve and tackle more and more complex challenges across various scientific disciplines.
AlphaFold AI model from Google DeepMind promises to speed up drug discovery
