An international research team led by the University of Waterloo is developing technology to dissolve painful kidney stones in the urinary tract using tiny robots. The research is published in the journal Advanced Healthcare Materials.

The new technique, tested in a life-size, 3D-printed model, features thin, spaghetti-like strips fitted with magnets that can be moved into place near uric acid kidney stones with a robotic arm operated by doctors.

The soft, flexible robot strips are about a centimeter long and contain an enzyme called urease. Once in place, the urease reduces the acidity of the surrounding urine, thereby dissolving stones until they are small enough to pass naturally in just a few days.

Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate.

KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data—opening up new possibilities for drug discovery. The research is published in the journal Advanced Science.

The research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein’s structure alone—without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.