University of Missouri researchers have released the world’s largest collection of protein models with quality assessment — a groundbreaking new resource that could accelerate drug development for diseases such as Alzheimer’s and cancer. The database, called PSBench, includes 1.4 million annotated protein structure models, all verified by independent experts. It gives scientists the reliable information they need to build more accurate artificial intelligence (AI) systems for assessing the quality of protein structure models, which is critical for developing future medical treatments.

As artificial intelligence grows more powerful, its energy demands are rising rapidly—driven by massive data centers that many experts say are unsustainable. To address this challenge, UT San Antonio is launching a national initiative through its MATRIX AI Consortium to rethink how AI computing works.

Backed by funding from the National Science Foundation, the initiative—called THOR: The Neuromorphic Commons—will establish a publicly accessible national hub for neuromorphic computing. This brain-inspired approach mimics how human neurons process information, activating only when needed and using far less energy than traditional computers.

THOR will provide researchers nationwide with open access to the largest full-stack neuromorphic computing platform of its kind, accelerating experimentation while advancing more energy-efficient and sustainable AI technologies.

MRI scans often struggle to capture clear images of deep or delicate tissues. Researchers at the Max Delbrück Center now report in “Advanced Materials” a new lightweight antenna that boosts image quality and may shorten scan times ­– without changing existing MRI machines.