G protein-coupled receptors sit in the plasma membrane, the boundary that defines the inside and outside of a living cell. They communicate with nearly every physiological process in our bodies, from the ability to see and smell, to sensing of adrenaline, insulin, nutrients and medicines. Researchers now report using AI to create tiny, computationally designed proteins to activate or block G protein-coupled receptors. The methods used in their study may form a roadmap for future efforts to create new drug candidates for metabolic, inflammatory, and neurological diseases. These approaches would try to target G protein-coupled receptors that have been largely inaccessible to conventional drug discovery.  

Researchers at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) have created a new tool that can catch and correct potential mistakes in real time while 3D printing large plastic parts. The automated system could help U.S. manufacturers produce large, custom parts with fewer defects, potentially reducing waste, lowering costs, and strengthening domestic competitiveness in additive manufacturing.

UC San Diego researchers report substantial reductions in symptoms of depression by using machine learning to tailor behavioral interventions based on data participants collect about their own moods and daily habits

Wiley today releases the Wiley Registry of Mass Spectral Data 2026, the new edition of one of the world’s largest and most trusted mass spectral reference databases for identifying unknown chemical compounds

Researchers developed NSYOLO, an AI framework that automatically segments and analyzes nanoparticles in electron microscopy images with high precision, improving accuracy in complex imaging environments and enabling high-throughput nanomaterial characterization.Researchers developed NSYOLO, an AI framework that automatically segments and analyzes nanoparticles in electron microscopy images with high precision, improving accuracy in complex imaging environments and enabling high-throughput nanomaterial characterization.

AI tools can accelerate soil science by speeding up early-stage work, improving predictions to support decisions on land-use, carbon, and climate adaptation, handling complex data, and freeing scientists to focus on questions that require expert judgment.