A Vanderbilt University-led research team has developed a cost-effective, AI-driven "protein language" model that could help scientists respond more quickly and effectively to emerging health threats, as well as lead to better health outcomes for diseases such as cancer.
The concepts that make large language models, or LLMs, work have been around since the early days of computing, according to IBM. However, today's artificial intelligence is accelerating our ability to find patterns in large volumes of data and more accurately predict outcomes.
The Vanderbilt University Medical Center team, which included scientists from the U.S., Sweden, and Australia, revealed in a media release that it trained a "protein language" model — dubbed MAGE — to recognize previously characterized antibodies and generate antibody sequences for avian flu and respiratory viruses without a starting template.
"This study is an important early milestone toward our ultimate goal — using computers to efficiently and effectively design novel biologics from scratch and translate them into the clinic," corresponding author Ivelin Georgiev, Ph.D., said in the release.
"Such approaches will have significant positive impact on public health and can be applied to a broad range of diseases, including cancer, autoimmunity, neurological diseases, and many others," Georgiev added. The researchers published their findings in the journal Cell.
More broadly, while AI's massive energy and water demands remain a matter of significant concern — as communities often end up paying the price in the form of higher electric bills, poorer air quality, and dirtier drinking water — the breakthrough underscores why innovators are so high on its potential. If done right, it could improve lives and even prevent catastrophe.
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"MAGE could be used to generate antibodies against an emerging health threat more rapidly than traditional antibody discovery methods that would rely on access to specialized biological materials (e.g., blood samples or antigen protein)," the researchers wrote.
As far as dealing with unwanted impacts of AI, many data centers are using clean energy to power operations, and a new cooling breakthrough could reduce AI's energy footprint. Nontoxic, less water-intensive cooling methods are also emerging. However, a lot of work clearly remains to lower pollution and resource consumption levels associated with AI-focused technologies.
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