Much of the recent AI hype has been attractive digital content Although it is generated from a simple prompt, there are also concerns about its ability. drastically reduce the workforce and carry out malicious propaganda much more convincing. (Fun!) However, some of the most promising, and potentially less creepy, things in AI do work. There is a lie in medicine.new update to Google's AlphaFold software This could lead to breakthroughs in research and treatments for new diseases.
AlphaFold Software, from Google Deep Mind And (also owned by Alphabet) isomorphic labhave already demonstrated that they can predict how proteins will fold with shocking accuracy.the Cataloged an astonishing 200 million known proteinsAnd Google says millions of researchers have used previous versions to make discoveries in areas such as malaria vaccines, cancer treatments, and enzyme design.
Knowing a protein's shape and structure determines how it interacts with the human body, allowing scientists to develop new drugs or improve existing ones. But the new version, AlphaFold 3, can model other important molecules, including DNA. It can also graph interactions between drugs and diseases, potentially opening exciting new doors for researchers. And Google says it achieves that accuracy with 50% better accuracy than existing models.
„AlphaFold 3 takes us beyond proteins to a wide range of biomolecules,“ says Google's DeepMind research team I wrote it in a blog post. “This leap could unlock more innovative science, from developing biorenewable materials and more resilient crops to accelerating drug design and genomics research.”
„How do proteins respond to DNA damage? How do they find it and repair it?“ John Jumper, Google DeepMind Project Leader Said wired. “We can start answering these questions.”
Before AI, scientists could only study the structure of proteins. electronic microscope and a complex method like X-ray crystal structure analysis. Machine learning streamlines much of that process by using patterns recognized in training (often imperceptible to humans or standard equipment) to predict the shape of proteins based on their amino acids. .
Google says some of AlphaFold 3's advances come from applying diffusion models to molecular predictions. Diffusion models are a central part of AI image generation, including: The middle of a journey, google gemini and OpenAI Darui 3. When these algorithms are incorporated into AlphaFold, “the molecular structures that the software generates become clearer.” wired explain. In other words, it takes a configuration that seems vague or ambiguous and makes educated guesses based on patterns in the training data to clarify it.
„This is a huge step forward for us,“ Google DeepMind CEO Demis Hassabis said. wired. „This is exactly what you need for drug discovery. You need to see how small molecules bind to drugs, how strongly they bind, and what else they bind to.“
AlphaFold 3 uses a color-coded scale to label the confidence level of predictions, allowing researchers to take appropriate precautions against results that are unlikely to be accurate. Blue means high reliability. Red means less certainty.
Google is developing AlphaFold 3 Free for researchers to use For non-commercial research. However, unlike past versions, the company has not open sourced the project. Professor David Baker of the University of Washington, one of the prominent researchers creating similar software, expressed his disappointment: wired Google has chosen that path. But he was also surprised by the software's capabilities. “AlphaFold 3's structural prediction performance is very impressive,” he said.
Regarding future developments, Google says, „Isomorphic Labs is already working with pharmaceutical companies to apply it to real-world drug design challenges and ultimately develop new treatments that change the lives of patients.“ It has said.