Do not go gentle into that good night, old age should burn and rave at close of day; Rage, rage against the dying of the light, though wise men at their end know dark is right, because their words had forked no lightning they, do not go gentle into that good night.
KAIST unveils homegrown AI model for predicting protein structures, drug binding
A promotional image of Korea Advanced Institute of Science and Technology's (KAIST) new biomolecular AI model "K-Fold" / Courtesy of KAIST
By Jhoo Dong-chan
Researchers at the Korea Advanced Institute of Science and Technology (KAIST) said Friday they have developed a biomolecular artificial intelligence (AI) model, called K-Fold, that predicts protein structures and how drug candidates bind to them, with the aim of speeding up new drug development using homegrown technology.
The model was built by a KAIST-led group called Team KAIST as part of a project run by Korea's Ministry of Science and ICT to develop AI foundation models specialized by field. KAIST President Bae Chung-sik said the project reflects the importance of "sovereign AI" — a country's ability to develop and control its own core technology — for national competitiveness in the AI era.
K-Fold is designed to predict not only the 3D shape of a single protein, but also how it interacts with other proteins, drug candidates, or genetic material such as DNA and RNA, and where and how a candidate compound binds.
That combination of structure prediction and binding prediction is central to early-stage drug discovery, in which researchers must identify a disease-related protein's shape before screening for substances likely to bind to it effectively.
In an internal evaluation in March, the team said K-Fold's accuracy in predicting complex molecular structures approached that of Google DeepMind's AlphaFold3, a leading international model.
A follow-up benchmark test in August found K-Fold outperformed existing global models on some measures, including binding predictions for G-protein-coupled receptors and kinases — both common drug targets in cancer and other diseases — as well as for targeted protein degradation, a newer drug approach that eliminates disease-causing proteins directly rather than merely blocking them.
The researchers said K-Fold also runs up to 25 times faster than comparable existing models because it skips a computationally heavy step, used by earlier AI models, of searching for and comparing large numbers of similar protein sequences before calculating structure.
KAIST-affiliated startup HITS has integrated K-Fold into HyperLab, a web-based AI research platform, allowing researchers to design drug candidates through natural-language requests rather than operating specialized software directly.
The Korea Pharmaceutical and Bio-Pharma Manufacturers Association and the Korea Biotechnology Industry Organization are helping promote the model's use in industry; the team plans to release K-Fold free of charge, with HyperLab moving from a beta test to wider commercial service later this year.
This article was published with the assistance of generative AI and edited by The Korea Times.