Alphabet’s DeepMind Takes Historic New Milestone in AI-Based Protein Structure Prediction
DeepMind, AI technology company that is part of Google’s parent company Alphabet has made a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold System has officially solved a major protein folding challenge that has baffled the scientific community for 50 years. The advancement of DeepMind’s AlphaFold capabilities could lead to a significant leap forward in areas such as our understanding of disease, as well as future drug discovery and development.
Essentially, the test AlphaFold passed shows that AI can correctly determine, with a very high degree of accuracy (accurate to the width of an atom, in fact), the structure of proteins in a matter of days – a very complex task. which is crucial in determining how diseases can best be treated, as well as in solving other big problems such as how best to break down environmentally hazardous materials like toxic waste. You may have heard of “Folding @ Home”, the program that allows people to bring their own home computer processing power (and formerly the game console) to protein folding experiments. This massive global crowdsourcing effort was necessary because, using traditional methods, portion-folding prediction takes years and is extremely expensive in terms of direct costs and IT resources.
DeepMind’s approach is to use an “attention-based neural network system” (essentially a neural network that can focus on specific inputs in order to increase efficiency). It is able to continuously refine its own predictive graph of possible protein folding outcomes based on their folding history, and provide highly accurate predictions as a result.
How proteins fold – or change from a random chain of amino acids as they are created to a complex 3D structure in their final stable form – is critical to understanding how diseases are transmitted, as well as how conditions work. common such as allergies. If you understand the folding process, you can potentially alter it, stopping the progression of an infection halfway through, or, conversely, correcting folding errors that can lead to neurodegenerative and cognitive impairment.
DeepMind’s technological leap could make the accurate prediction of these folds a much less time-consuming and resource-consuming process, which could dramatically change the rate at which our understanding of diseases and treatments progresses. This could be useful in addressing major global threats, including potential future pandemics like the COVID-19 crisis we are currently experiencing, by predicting the structures of viral proteins with a high degree of accuracy at the onset of the onset of any. new future threat like SARS-CoV-2, thus accelerating the development of effective treatments and potential vaccines.