Google DeepMind has unveiled the third version of its AlphaFold Artificial Intelligence (AI) model that aims to help scientists target disease more effectively and create medications.
As per Google’s blog post and a paper published in the journal Nature “the AlphaFold 3 is a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy.”
Researchers can use the AlphaFold 3 AI model for free. It will allow the scientist to test various structures made of DNA, RNA, and Proteins with an aim of providing predictions about how these substances may interact. Using AlphaFold 3 for doing this type of predictive modeling might reduce expenses and improve research.
To build AlphaFold 3’s potential, Isomorphic Labs, a sister company to Google DeepMinds is “already collaborating with pharmaceutical companies to apply it to the real-world drug design challenges, and ultimately, developing new life-changing treatments for patients.”
Apart from that, Google DeepMinds also launched a new AI tool, the AlphaFold Server that will help scientists make new hypotheses to test in the lab, speeding up workflows and enabling new innovations.
Senior Research Scientist at DeepMind, John Bumper said, “It’s going to be very important how much easier the AlphaFold server makes it for the biologists- who are experts in biology, not computer science, more complex cases,” as per Reuters.
In 2020, the company launched the AlphaFold 2 which achieved a significant milestone in the prediction of a protein structure. According to Google, millions of scientists used the AlphaFold 2 model worldwide to make innovations in fields such as malaria vaccines, enzyme design, and cancer treatments.
Google says that AlphaFold 3 “takes us beyond proteins to a broad spectrum of molecules” which could lead to the development of more innovative technologies to speed up medicines design and genomics research.
The tech giant in their blog stated, “ The impact of AlphaFold3 and AlphaFold Server will be recognized through how they empower researchers and scientists to speed up discovery across open questions in biology and new lines of research.”
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