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Using artificial intelligence and supercomputers, researchers have found overlooked MXene materials in old databases and created new ones with a faster, cleaner method.

Researchers at the University of Washington have computationally designed synthetic proteases - enzymes that precisely cut other proteins at chosen sequences. Two studies published in Nature describe zinc-based metalloproteases and cysteine proteases created entirely from scratch using deep learning. These designer enzymes show rate enhancements millions to hundreds of millions of times faster than uncatalyzed reactions, and they work inside living cells. The work opens paths to destroying disease proteins, clearing misfolded proteins from neurodegenerative diseases, and activating drugs only in targeted tissues.

OpenAI announced AI-generated proofs in mathematics, computer science, and physics, and is experimenting with expert-led collaborations to turn raw model output into verified scientific understanding.

Researchers at Chalmers University of Technology have developed an AI system that performs the full cycle of scientific research on its own. The AI scientist generates hypotheses, designs experiments, and interprets results with minimal human input. The system was tested on brewer's yeast, demonstrating a closed-loop approach to biological discovery.

Google Research has developed a framework called VERID that connects AI agents to physical laboratory robots, enabling closed-loop molecular design cycles. The system combines a digital twin of laboratory equipment with machine learning to run experiments, learn from results, and self-improve.

ORCA Computing unveils its PT-3 photonic quantum computer, reporting gains in AI-driven protein discovery and industrial optimization through hybrid quantum-classical computing.

Scientists’ judgments could help automated microscopes pursue discoveries that conventional numerical targets overlook.