-
KAIST Reveals Principle Behind Ultra-Fast DNA Repair, Like “Finding a Needle in Seoul”
<(From Upper Left) Professor Ja Yil Lee, Professor Gwangrog Lee, Professor Jejoong Yoo, (From Bottom Left) Ph.D candidate Subin Kim, Dr. Donghun Lee, Ph.D candidate Gyeongpil Jo>
DNA is the blueprint of the human body. However, tens of thousands of DNA lesions occur in our bodies every day. In particular, if “apurinic/apyrimidinic sites” (AP sites, damaged sites where one letter of DNA information has been erased) are not properly repaired, they can lead to cancer and aging. Finding these tiny damaged sites within the vast genome is as difficult as “finding a single needle in Seoul.” Korean researchers have uncovered the secret of how a DNA repair enzyme rapidly searches for damaged sites by sliding along DNA.
KAIST (President Kwang Hyung Lee) announced on the 4th of June that a research team led by Professor Gwangrog Lee of the Department of Biological Sciences, together with Professor Ja Yil Lee’s team at UNIST (President Jong Rae Park) and Professor Jejoong Yoo’s team at Sungkyunkwan University (President Jibeom Yoo), has identified the precise molecular mechanism by which the DNA repair enzyme “APE1” (apurinic/apyrimidinic endonuclease 1, an enzyme that recognizes DNA damage sites and initiates repair) detects damaged DNA.
The research team tracked the movement of APE1 in real time by combining single-molecule FRET (smFRET, an analytical technique that observes the movement and structural changes of single biomolecules in real time), DNA curtain technology (a technique that aligns multiple strands of DNA to observe their interactions with proteins), and molecular dynamics (MD, a simulation method that calculates molecular movement using computers).
As a result, the team found that APE1 does not search DNA randomly, but instead uses a “one-dimensional diffusion” strategy (a method of searching by moving along the DNA strand), sliding along the DNA to find damaged sites.
This is similar to an intelligent inspection robot moving through a maze-like network of underground pipes beneath a huge city to detect a tiny leak. Instead of searching aimlessly from place to place, the enzyme moves efficiently along the “genomic highway” of DNA to quickly locate damaged sites.
In particular, the research team also found that the enzyme’s flexible end region, known as an “intrinsically disordered region” (IDR, a protein segment that moves freely without a fixed structure), plays a key role in the DNA search process.
This intrinsically disordered region acts like a hook that holds onto DNA, helping APE1 remain on the DNA and move along it for a long time without falling off. In fact, when the research team removed this region, the enzyme’s ability to find damaged sites decreased by more than fivefold.
The researchers also confirmed that magnesium ions (Mg²⁺, metal ions that assist various enzymatic reactions inside cells) are not merely auxiliary factors, but key elements that increase the efficiency of DNA search. Magnesium ions were found to stabilize the binding between APE1 and DNA, helping the enzyme move more effectively along DNA.
< Research Image (AI-Generated Image) >
Professor Gwangrog Lee of KAIST explained, “This study identified the mechanism by which a biomolecule rapidly searches for DNA damage through an intrinsically disordered region (IDR), and then operates precisely through a structured region,” adding, “This principle could provide a key clue for developing next-generation anticancer drugs that disable DNA repair functions in cancer cells, as well as for research on suppressing aging.” Professor Ja Yil Lee of UNIST emphasized, “This study is significant in that it revealed that an intrinsically disordered region, which moves flexibly without a fixed structure and interacts with various molecules, plays a key role in finding DNA damage sites.”
This study, with KAIST Dr. Donghun Lee, UNIST doctoral student Subin Kim, and Sungkyunkwan University doctoral student Gyeongpil Jo as co-first authors, was published on May 14 in the world-renowned international journal Nucleic Acids Research.
※ Paper title: “APE1 Coordinates Its Disordered Region and Metal Cofactors to Drive Genome Surveillance,” DOI: org/10.1093/nar/gkag479
This research was supported by the KAIST Grand Challenge 30 Project (KC30), the National Research Foundation of Korea’s Core Synthetic Biology Technology Development Program, Mid-Career Researcher Program, Basic Research Laboratory Program, the Korea Drug Development Fund’s Drug Development Foundation Expansion Research Program, the Institute for Basic Science (IBS), and the Institute of Information & Communications Technology Planning & Evaluation (IITP)’s Advanced AI Source Technology Development Program.
2026.06.04 View 580 -
Fatty Acid in Body Acts as Natural Brake Suppressing Cancer Cell Growth
< (Left) Professor Seyun Kim from KAIST, (Right) Professor Young-Joo Byun from Korea University >
'mTOR', a protein in our body, becomes excessively activated in cancer cells, promoting cell growth and metastasis. Korean researchers have discovered for the first time in the world that '13-HODE'—a substance produced when fatty acids, which are abundant in vegetable oils, are metabolized in the body—binds directly to mTOR and acts as a 'natural brake' that suppresses cancer cell growth. This research presents the possibility of developing next-generation anticancer treatment strategies. Our university announced on the 2nd that a joint research team led by Professor Seyun Kim from the Department of Biological Sciences and Professor Young-Joo Byun from the College of Pharmacy at Korea University (President Dong-One Kim) has discovered that the lipid metabolite '13-HODE' (a lipid metabolite produced when fatty acids are metabolized) suppresses the activity of mTOR, a key regulatory factor in cancer cell growth. In addition, this research involved joint participation from Professor Mi Young Kim from the Department of Biological Sciences at KAIST, Professor Byung-Chul Oh from the College of Medicine at Gachon University (President Gil-ya Lee), and Professor Patrick L. Wintrode and Professor Daniel Deredge from the School of Pharmacy at the University of Maryland, USA. mTOR is an important enzyme (a protein that helps biological reactions) that regulates cell growth and energy usage. However, in cancer cells, mTOR activity is known to increase abnormally, promoting cell proliferation and metastasis. For this reason, anticancer research aimed at controlling mTOR is being actively conducted worldwide. The research team focused on substances capable of binding to the mTOR protein, particularly natural metabolites produced by the body itself. Through extensive metabolite screening (a technology that analyzes large quantities of metabolites in vivo), they discovered that a lipid metabolite called '13-HODE', which is formed as fat changes in the body, attaches directly to the active site of the mTOR protein and stops its operation in cancer cells.
< (AI Image) Cancer cell growth suppression effect based on direct inhibition of mTOR by linoleic acid-derived 13-HODE >
The 13-HODE (13-Hydroxyoctadecadienoic acid) molecule is produced in our body during the process of metabolizing linoleic acid (an essential unsaturated fatty acid), which is abundant in vegetable oils. In this process, 'ALOX15 (an enzyme that induces a fatty acid oxidation reaction)' oxidizes linoleic acid to produce 13-HODE. The core of this research goes beyond the simple level of showing that 13-HODE has anticancer efficacy; it clarifies the molecular mechanism (the biological principle of operation) by which 13-HODE physically binds directly to the mTOR protein to fundamentally block its function. The research team verified this through molecular docking simulations (computer-based analysis of molecular interactions) and mass spectrometry (a technology that analyzes the mass and structure of molecules). The research team also confirmed that 13-HODE concentrations are extremely low in breast and colorectal cancer cells. This was found to be due to a decrease in the expression (the process by which genetic information is actually made into protein) of the ALOX15 enzyme required for 13-HODE generation. The research team proved that increasing the production of ALOX15 and 13-HODE reduces mTOR activity and suppresses cancer cell growth. Professor Seyun Kim said, "This research is significant in that it revealed that lipid metabolites generated within the human body can directly inhibit mTOR, a core protein for cancer growth. It can be utilized not only for new anticancer treatment strategies leveraging lipid metabolism but also for developing treatments that regulate mTOR overactivation observed during inflammation and aging processes."
Professor Young-Joo Byun from the College of Pharmacy at Korea University, who co-led the joint research, said, "This research is a study that clarified the interaction between proteins and fatty acid metabolites at the molecular level through the convergence of biological sciences and pharmacy. It will serve as an important foundation for the development of innovative new drugs in the future." Professor Jie Chen from the University of Illinois, USA, a world-renowned authority in the field of mTOR research, evaluated it in a journal preview as "an outstanding discovery that presents a new breakthrough in cancer cell control." This research, with Dr. Seung Ju Park and Ph.D. student Sera Kim from the Department of Biological Sciences at KAIST participating as co-first authors, and Professor Young-Joo Byun from the College of Pharmacy at Korea University and Professor Seyun Kim from the Department of Biological Sciences at KAIST participating as co-corresponding authors, was published on May 21st in the international academic journal in the field of chemical biology, Cell Chemical Biology. Furthermore, in recognition of its importance, it was selected as the cover article for the May issue of the journal. ※ Paper Title: Mechanism by which a linoleic acid metabolite suppresses cancer cell growth by inhibiting mTOR, DOI: https://doi.org/10.1016/j.chembiol.2026.04.004 ※ Author Information: Seung Ju Park, Sera Kim, Hongmok Kwon, Jiyeon Choi, Ji Kwang Kim, Inhong Jung, Seol-Wa Lim, Young Ran Kim, A-Yeong Yang, Boah Lee, Haein Lee, Seung Eun Park, Seulgi Lee, Myeongsu Shin, Bernie Byunghoon Park, YunHye Kim, Jinwook Lee, Byung-Chul Oh, Daniel Deredge, Patrick L. Wintrode, … Seyun Kim
< Cover Article of the Journal Cell Chemical Biology May Issue >
Meanwhile, this research was conducted with support from the Samsung Science and Technology Foundation, the Mid-Career Research Program, the Basic Research Laboratory of the National Research Foundation of Korea, the Leading Research Center, the KAIST Quantum+X Interdisciplinary Convergence Technology Development Project, the KAIST Grand Challenge Project, and the Ministry of Education's Core Research Institute Program.
2026.06.04 View 623 -
Unveil Next-Gen Spatial AI and XR Core Technologies at KMF 2026
< Poster of the Korea Metaverse Festival (KMF) >
KAIST announced on June 5 that its Graduate School of Metaverse will participate in the Korea Metaverse Festival (KMF) 2026, held at COEX in Seoul from June 10 to 12. During the event, the school will showcase its core research achievements in "Next-Generation Spatial AI" and XR (Extended Reality)—technologies designed to recognize and understand physical spaces, analyze the positions, movements, and contexts of people and objects, and enable seamless interaction.
These achievements are evaluated as representative outcomes of the "Graduate School of eXtended Reality Support Program," an initiative funded by the Ministry of Science and ICT (MSIT) and the Institute for Information & Communications Technology Planning & Evaluation (IITP) to foster top-tier talents for future core industries.
At IEEE VR 2026, the world’s most prestigious academic conference in virtual reality held earlier this year, the KAIST Graduate School of Metaverse presented 12 oral papers—the second highest among universities and research institutes worldwide—proving its top-tier global research competitiveness.
The Graduate School of eXtended Reality Support Program is recognized as a preemptive policy designed to systematically nurture master’s and doctoral-level specialists in key future fields, such as XR, digital twins, and spatial computing, ahead of full-scale market formation. Through this initiative, world-class research outcomes continue to emerge in the fields of Spatial AI, Human-Computer Interaction (HCI), and virtual convergence systems, contributing significantly to national talent development and the accumulation of technological capabilities.
On June 10, the first day of the event, the "2026 Virtual Convergence Innovative Talent Symposium and Achievement Sharing Tech Day" will introduce a large number of next-generation research results in the XR and Spatial AI sectors. The KAIST Graduate School of Metaverse plans to showcase its key research accomplishments, focusing on next-generation immersive interaction technologies and industry-aligned digital twin demonstration cases.
The flagship technologies to be unveiled on-site include:
OFERA: A real-time avatar facial expression reproduction system that naturally reconstructs facial expressions covered by XR devices, enhancing immersion in remote meetings and virtual collaborations.
AquaHaptics: An immersive underwater tactile interaction technology that transmits the water resistance and texture of a virtual underwater environment directly to the user's fingertips.
Multi-sensor-based Cultural Heritage Digital Twin and AR Visualization Technology: A solution that allows users to inspect even the internal defects of cultural heritage assets using 3D and AR visualization.
< Real-time 3D Gaussian avatar control technology for expressing facial expressions covered by an HMD >
< Multi-modal fluid-haptic rendering technology ‘AquaHaptics’ for highly immersive virtual haptic experiences >
In addition, various other research highlights will be presented, including an indirect tactile feedback system that utilizes smartwatches during bare-hand XR interactions, and "ForceCtrl," an XR raycasting technique that allows users to intuitively select and manipulate desired objects in virtual space using the force of their hand gestures.
The KAIST Graduate School of Metaverse, centered around the Post-Metaverse Research Center (PMRC), is also driving the establishment of the "Bridge Time and Space (BTS)" platform—a hyper-spatiotemporal virtual convergence platform designed to accumulate and share XR experiences and interaction data.
Furthermore, in collaboration with domestic and international research institutes such as New York University (NYU), ETRI, and KISTI, the school is developing an "XR Experience Sharing Platform." By the end of this year, it plans to launch the "New Jam Daejeon" project, a real-world demonstration that combines K-culture with XR technology.
Woontack Woo, Head of the KAIST Graduate School of Metaverse, stated, "Spatial AI and XR will serve as the core infrastructure for the future virtual convergence industry. Based on our world-class research outcomes, KAIST will connect the 'assetization of XR experiences and knowledge' with the virtual convergence industry to drive tangible innovation."
Meanwhile, the KAIST Graduate School of Metaverse will operate a dedicated exhibition booth during KMF 2026 to conduct live demonstrations of its core technologies. It will also host an admission information session for the graduate school and introduce its industry-academic joint research and technological cooperation programs.
2026.06.04 View 832 -
Professor Hoon Sohn of the Department of Civil and Environmental Engineering Selected as the June Winner of the 'Korea Scientist and Engineer Award'
Professor Hoon Sohn from KAIST Department of Civil and Environmental Engineering has been selected as the June winner of the 'Korea Scientist and Engineer Award.'
The Korea Scientist and Engineer Award is presented monthly by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) to a researcher who has made significant contributions to the advancement of science and technology through original research achievements over the past three years. The award includes a commendation from the Deputy Prime Minister and Minister of Science and ICT, along with a cash prize of 10 million KRW.
Professor Hoon Sohn was recognized for his contributions to developing an affordable, high-precision displacement sensor technology capable of detecting disaster and hazard risks in small-to-medium-sized infrastructure in real-time.
With the rapid aging of infrastructure such as bridges and buildings in recent years, the importance of technology that continuously monitors the structural safety of facilities has been growing. However, small-to-medium-sized structures—which make up the vast majority of infrastructure worldwide—exhibit very subtle movements on a millimeter scale, requiring highly precise measurement. Moreover, existing equipment is prohibitively expensive, making widespread adoption difficult.
To overcome these limitations, Professor Sohn combined millimeter-wave (mmWave) radar with Micro-Electro-Mechanical Systems (MEMS) accelerometers and applied signal processing algorithms. Through this, he successfully developed a technology that can simultaneously measure a structure's vibration, tilt, and displacement with a single sensor.
The production cost of this sensor is under 1 million KRW, which is approximately 1/40th the cost of conventional equipment, yet it boasts a high precision of 0.026 mm. Its power consumption has also been reduced to 1/100th of existing systems. Furthermore, it incorporates energy harvesting technology that utilizes ambient wasted energy, allowing for completely wireless operation.
The reliability of this technology has been proven through field demonstrations at more than 13 domestic and international sites, including a parking garage at Stanford University (USA), a highway in San Jose (USA), a bridge in Weifang (China), and the Geumgang Pedestrian Bridge in Sejong (South Korea).
Professor Sohn stated, "The significance of this research lies in establishing a technological foundation to precisely manage small-to-medium-sized structures that have previously been excluded from continuous, routine monitoring." He added, "Moving forward, I will continue my research on AI-based digital twins to lead the automation, unmanned operation, and intelligent advancement of the safety diagnosis market, thereby contributing to public safety and disaster prevention."
2026.06.04 View 755 -
KAIST Develops New Catalyst Design Technology to Improve Battery and Hydrogen Fuel Cell Performance
<(From Left) Professor Seung Jun Hwang, Professor Jaeyune Ryu>
Korean researchers have developed a new catalyst design technology that can improve the performance of batteries and hydrogen fuel cells while reducing energy loss.
KAISTannounced on the 1st of June that a research team led by Professor Seung Jun Hwang of the Department of Chemistry, through joint research with Professor Jaeyune Ryu’s team from the Department of Chemical and Biological Engineering at Seoul National University , has proposed a new catalyst design strategy that can improve the efficiency of key reactions that generate electricity inside batteries and fuel cells.
A catalyst is a material that helps chemical reactions occur faster and more efficiently. In batteries or fuel cells, it plays a role in facilitating the reactions that generate electricity. Catalysts usually consist of a central metal and a molecular structure surrounding it.
In previous studies, methods mainly involved changing the type of metal from iron (Fe) to cobalt (Co) or nickel (Ni), or newly designing the molecular structure around the metal, known as the ligand, to improve reaction performance. In simple terms, this approach changes the material or shape of the catalyst itself to make it react better. By contrast, this study is differentiated by showing that performance can be improved simply by adjusting the electrical environment around the catalyst, without greatly changing the catalyst itself.
<(AI Image) Visualization of Enhanced Fe Porphyrin Catalyst Reactivity Induced by the Electric Field of Metal Cations>
To use a simple analogy, this study can be compared to “making cooking work better by adjusting the kitchen environment instead of changing the cooking tool itself.” Previous catalyst research was closer to changing the material of a frying pan or redesigning its shape. By contrast, this study keeps the frying pan the same and precisely adjusts the surrounding temperature and airflow so that the food cooks better. In other words, the core of this research is that the team made the reaction occur more efficiently by adjusting the electrical environment around the catalyst, rather than creating an entirely new catalyst.
The research team confirmed that placing “cations (+)” around the catalyst to create a very small electric field can induce the reaction needed to generate electricity to occur more stably. In particular, the proportion of the desired reaction increased from the previous level of about 12% to as high as 52%.
Through this, the research team confirmed that the desired reaction can be efficiently induced with less energy than before. This is expected to contribute to improving the efficiency, lifespan, and stability of batteries and hydrogen fuel cells.
The oxygen reduction reaction (ORR, a key reaction in which oxygen receives electrons to generate electricity) examined in this study is a core reaction that generates electricity in next-generation energy devices such as fuel cells for hydrogen vehicles (Fuel Cell, a device that produces electricity through a chemical reaction between hydrogen and oxygen) and metal-air batteries (Metal-Air Battery, a next-generation battery that stores and produces electricity using metal and oxygen in the air).
The research team also believes that this principle can be applied to catalyst technologies that convert carbon dioxide (CO₂) or hydrogen into other useful substances, and that it can therefore be used in the development of various next-generation energy catalysts, including carbon dioxide reduction technologies and eco-friendly hydrogen production technologies.
<Schematic Illustration of Cation-Mediated Regulation of ORR Catalyst Activity>
<(AI Image) Schematic Illustration of Cation-Mediated Regulation of ORR Catalyst Activity>
Professor Seung Jun Hwang stated, “This study demonstrates that reaction properties can be precisely controlled solely through the surrounding electrical environment, without changing the structure of the catalyst itself,” adding, “We expect it to present a new direction for developing next-generation batteries, fuel cells, and eco-friendly energy catalyst technologies.”
This research, with POSTECH chemistry doctoral students Hwi Yul Jo and Vom Kang and KAIST postdoctoral researcher Dongyoung Kim as co-first authors, was published online on April 12 in the Journal of the American Chemical Society (JACS).
※ Paper title: “Localized Cation Unlocks Unique Activity–Selectivity Trends in Molecular Oxygen Reduction Catalysis,” DOI: pubs.acs.org/doi/10.1021/jacs.5c18246
Lead author information: Hwi Yul Jo (doctoral student, POSTECH), Vom Kang (integrated master’s–PhD student, POSTECH), Dongyoung Kim (postdoctoral researcher, KAIST)
This research was supported by the Samsung Science and Technology Foundation, the National Research Foundation of Korea’s “Hanwoomul” Basic Research Program, and the Nano and Material Technology Development Program.
2026.06.01 View 714 -
AI College Vision Declaration Ceremony Held... Presenting Vision for Fostering Global AI Convergence Talent
< KAIST College of AI Vision Declaration Ceremony >
KAIST held the 'KAIST College of AI Vision Declaration Ceremony' on the morning of the 1st at 10:00 AM in the Jeong Geun-mo Conference Hall on the 5th floor of the KAIST Academic Cultural Complex (E9). This event was organized to share both internally and externally the vision and future directions for nurturing core talent to lead the AI era, innovating education and research, fostering industrial cooperation, and establishing a responsible AI ecosystem.
The KAIST College of AI views artificial intelligence not merely as a tool for application, but as the foundation for generating new knowledge that drives transformation across science, technology, industry, education, and society as a whole. Accordingly, it plans to nurture both research talent to lead core AI technologies and convergence talent to creatively apply AI in various fields. Furthermore, it aims to establish an educational and research system that covers models, algorithms, systems, infrastructure, and domain convergence, as well as future society design and responsible AI.
The Vision Declaration Ceremony began with a welcoming address by Kwang Hyung Lee, President of KAIST. Following this, Kyeong Hoon Bae, Vice President and Minister of Science and ICT, delivered a keynote speech presenting directions for core talent development and educational innovation in the AI era. In the main session, Kuk-Jin Yoon, Dean of the KAIST College of AI, presented the college’s mid-to-long-term vision and key strategic initiatives under the theme of 'Vision and Innovation Directions of the KAIST College of AI.'
Notably, the appointment ceremony for the 'KAIST College of AI Advisory Board' was also conducted during this event. The advisory board will take on strategic advisory roles for the KAIST College of AI's education, research, industrial cooperation, global collaboration, and the implementation of responsible AI.
As for overseas advisory members, world-renowned AI scholars Yoshua Bengio, a professor at the University of Montreal, and Kyunghyun Cho, a professor at New York University, participated. Domestically, representatives from the Korea Institute of Science and Technology (KIST), as well as Crafton, Hyundai Motor Company / 42dot, Inable Fusion, Lunit, NAVER Cloud, NC AI, Rebellion, Samsung Electronics, SK Telecom, and Upstage, along with other major domestic AI/ICT companies and research institutions, took part.
In tandem, the 'KAIST AI Innovation Special Session' was held under the theme of 'New Education and Research Grammar in the AI Era.' The KAIST College of AI views students not merely as beneficiaries of education, but as active participants who design future learning methods and research cultures together. Accordingly, undergraduate student representatives directly took the stage as presenters to propose new possibilities for university education, which was followed by a panel discussion joined by the Dean of the College of AI, advisory board members, and students.
Kuk-Jin Yoon, Dean of the KAIST College of AI, stated, "The KAIST College of AI is not an organization that simply teaches AI technology, but aims to become an educational and research platform that expands human intellectual capacity and designs new knowledge and the future alongside AI. With this Vision Declaration Ceremony as a turning point, we will grow into a hub that leads world-class AI talent cultivation, challenging research, industry and social problem-solving, and the establishment of a responsible AI ecosystem."
Kwang Hyung Lee, President of KAIST, remarked, "AI is now transcending being a technology in a specific field and is becoming a core engine driving change across science, technology, industry, and society as a whole. We will actively support the KAIST College of AI so that it can lead AI talent cultivation and research innovation in South Korea and grow into an open platform that collaborates with the world."
Kyeong Hoon Bae, Vice President, stated, "To preemptively respond to a period of great transformation where AI is moving beyond the generation phase and into the execution phase, investment in AI talent is the most urgent priority. Through active communication with students, who are the consumers of education, we will build a differentiated AI educational system for South Korea."
< Poster for the KAIST College of AI Vision Declaration Ceremony >
2026.06.01 View 464 -
KAIST Produces Eco-Friendly Core Nylon Precursors Used from Clothing to Automobiles with Microbes
<(From Left) Dr. Da-Hee Ahn, Distinguished Professor Sang Yup Lee>
Nylon is a representative plastic material used throughout our daily lives, from clothing to automobiles. However, most of its raw materials have been produced through petrochemical processes, resulting in large carbon emissions. KAIST researchers have developed a technology that can produce key nylon precursors in an eco-friendly way using microbes.
KAIST (President Kwang Hyung Lee) announced on the 31st of May that a research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering has developed an Escherichia coli-based modular platform capable of producing three key monomers (basic molecular units that make up polymers) of “nylon 6,6” and “nylon 6” — adipic acid, hexamethylenediamine, and epsilon-caprolactam — from “glycerol (an eco-friendly bio-based byproduct generated during biodiesel production),” a renewable carbon source, using systems metabolic engineering (a technology that designs and optimizes microbial metabolic pathways to maximize the production of desired substances).
“Nylon 6” is highly flexible and is used in clothing and films, while “nylon 6,6” has excellent strength and heat resistance and is used in automobiles and machinery parts. The numbers after the nylon name indicate the number of carbon atoms contained in the raw material molecules.
The core of this study is that the biosynthetic pathway was divided into upstream and downstream modules, with E. coli strains assigned different roles. The upstream strain was designed to produce adipic acid from glycerol, while the downstream strain was designed to convert it into hexamethylenediamine or epsilon-caprolactam, respectively. Through this, the research team succeeded in producing adipic acid and hexamethylenediamine, the key raw materials of nylon 6,6, and epsilon-caprolactam, the key raw material of nylon 6, within a single integrated platform.
To improve production efficiency, the researchers compared and validated various enzymes (proteins that promote chemical reactions in living organisms), including carboxylic acid reductases and transaminases, and applied the optimal combination, thereby improving hexamethylenediamine titer. In addition, in the epsilon-caprolactam production process, they designed a flexible-linker fusion enzyme that enhances reaction efficiency through efficient cofactor regeneration.In the upstream module, the team reconstructed the biosynthetic pathway (a series of reaction processes through which compounds are produced in living organisms) and improved the performance of key enzymes using artificial intelligence (AI), increasing production titer. As a result, they succeeded in producing adipic acid at a level of 6 grams per liter (g/L) in a fed-batch fermentation process.
The research team also applied a “delayed inoculation” strategy (time-staggered co-culture), in which the second strain is introduced later after sufficient adipic acid has first been produced, rather than adding the two types of E. coli simultaneously. This is a method of sequentially introducing microbes with different roles at different times.
When this strategy was applied to a fed-batch fermentation process (a fermentation method that increases productivity by supplying nutrients step by step), the team produced 230 milligrams per liter (mg/L) of hexamethylenediamine and 808 micrograms per liter (μg/L) of epsilon-caprolactam using only glycerol. Although the production amounts are not yet high, the research team explained that these results represent world-class performance among cases of direct production from glycerol.
<Schematic Diagram>
This technology is significant in that it presents the possibility of producing nylon raw materials, which have relied on petrochemical processes, through bio-based methods.
The research team plans to further improve titer by combining AI-based enzyme design with additional systems metabolic engineering, and to expand the platform to produce various polymer raw materials (substances formed by the repeated bonding of multiple monomers).
Distinguished Professor Sang Yup Lee stated, “This study is meaningful in that it presents a modular microbial platform capable of producing key monomers required for nylon 6 and nylon 6,6 production from renewable carbon sources,” adding, “We will continue to advance enzyme and metabolic flux engineering to improve titer and develop this into a core platform for sustainably producing various bio-based polymer raw materials.”
The results of this study were published on May 4 in the Proceedings of the National Academy of Sciences (PNAS), with Dr. Da-Hee Ahn of the Department of Chemical and Biomolecular Engineering as the first author.
※ Paper title: “Metabolic engineering of Escherichia coli for the biosynthesis of nylon 6 and nylon 6,6 monomers”
Authors: Sang Yup Lee (KAIST, corresponding author), Da-Hee Ahn (KAIST, first author), Tong Un Chae (KAIST, second author), total of 3 authors
DOI: https://doi.org/10.1073/pnas.2535786123
This research was supported by the “Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries” project under the Petroleum Replacement Eco-Friendly Chemical Technology Development Program supported by the Ministry of Science and ICT, and by the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project under the Core Synthetic Biology Technology Development Program.
2026.06.01 View 1011 -
"Development of 'ADvisor', an AI that Predicts Instagram Advertising Performance in Advance"
<(Bottom from left) M.S candidate Gyurim Hwang, M.S candidate Yeongho Kim, Ph.D. candidate Kyungho Kim, Ph.D.candidate Jongha Lee, M.S candidate Yeonje Choi (Top from left) Undergraduate student Sejin Chung, Researcher Hongseok Lee, Researcher Myeong Ho Song, Ph.D. candidate Sunwoo Kim, M.S candidate Juyeon Kim, Professor Kijung Shin>
Social media advertising usually requires running multiple ad drafts in practice before determining which ad is effective. Because of this, testing advertisements demands significant time and costs. Furthermore, the criteria for an effective advertisement vary greatly by brand. While some brands prefer person-centered advertisements, others receive better responses from advertisements that emphasize actual usage scenes. However, these effective advertising strategies for each brand are often not clearly defined in the field, which has limited the technology to systematically reflect them and predict advertising performance.
To solve this problem, a research team led by Professor Kijung Shin at KAIST, in collaboration with the AI marketing company MADUP, developed 'ADvisor', an AI technology that predicts advertising performance for each brand.
ADvisor utilizes a generative vision-language model that understands images and text simultaneously to find different advertising success criteria for each brand and predict advertising effectiveness based on them. To achieve this, it not only analyzes the characteristics of the brand but also considers advertising data from other brands with similar tendencies for new brands that do not have sufficient advertising data to derive advertising strategies. Through this process, it can identify distinct advertising success criteria for each brand; for instance, a "strong headline phrase" is analyzed as an important criterion for a specific fashion brand, while "logo exposure" acts as a key element for another brand. Afterward, ADvisor evaluates the advertisement based on the derived criteria for each brand, reviews the evaluation results on its own, and repeatedly compensates for deficiencies to make the final prediction.
The research team verified the technology's performance using data from 10 brands in the beauty, fashion, and platform sectors collected through actual marketing campaigns. As a result, ADvisor recorded up to 7.2% higher performance compared to existing AI advertising prediction models. In particular, in an online A/B test conducted in a real Instagram advertising environment, it achieved an average of 27% better performance in key indicators such as click-through rate (CTR), cost per click (CPC), and return on ad spend (ROAS) than advertisements selected by field marketing experts, proving that it can be utilized in actual marketing decision-making.
Professor Kijung Shin stated, "Predicting advertising performance in advance is the first step for effective advertisement production," adding, "In the future, we will develop our research in a direction where AI directly generates and optimizes advertisements tailored to brand characteristics."
The study, in which Ph.D candidate Kyungho Kim and M.S candidate Yeonje Choi from the KAIST Kim Jaechul Graduate School of AI participated as co-first authors, was published online on April 18 in the Industry Track of ACL 2026, one of the most prestigious international academic conferences in the field of natural language processing. It has been accepted as an oral presentation paper and is scheduled to be presented in the United States this coming July.
※ Paper Title: Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs ※ Paper Link: https://openreview.net/forum?id=il84gAzAxx
Meanwhile, this research is an achievement of the project 'EntireDB2AI: Deep Representation Learning and Prediction Source Technology and Software Development Utilizing Entire Relational Databases Comprehensively', supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP).
2026.06.01 View 401 -
4 Institute of Science and Technolgies Form Alliance to Support Global Expansion of Deep-Tech Startups
An opportunity is opening up for homegrown deep-tech startups that have built strong technological capabilities to validate their competitiveness in the global market. KAIST announced on June 1st that, in collaboration with GIST, DGIST , and UNIST and with the sponsorship of the Ministry of Science and ICT, it is co-operating the ‘2026 Emerging Tech Global Launchpad’—a program supporting overseas local Proof of Concept (PoC, technological verification), investment attraction, and global networking—and is currently recruiting participating companies. The application period runs from May 29th to June 19th. This program has been designed to support ‘emerging tech-based startups’ in achieving tangible outcomes in the global market. Based on each company’s stage of readiness and target market, the program divides its support methods and leverages the regional startup networks and overseas cooperative infrastructure held by the 4 Institutes of Science and Technology (ISTs) to facilitate global market entry.
Emerging Tech Startups: Innovative companies possessing core new technologies for future industries—such as Artificial Intelligence (AI), quantum technology, next-generation energy, and biotech—and preparing for commercialization in the global market. The support types are divided into two tracks. Track 1, the ‘Global Expansion Track,’ is designed for companies that are already preparing for or attempting to enter overseas markets. Selected companies will participate in local overseas PoCs to verify the market fit of their technology for local customers and its commercial viability. They will also receive support for local partner-linked verification environments and expert networks. Track 2, the ‘Global Readiness Track,’ is a process for companies looking to build their capacity for overseas expansion.
This track provides PoC-tailored education and intensive business commercialization support programs, focusing on enhancing product and customer discovery strategies for overseas markets, designing investment attraction scenarios, and mastering local market entry methodologies. In particular, following the specialized PoC acceleration, the program will connect participants to international conferences held in major innovation hubs—such as the US West Coast, US East Coast, and Singapore—to support the discovery of global PoC customers.
Through this, participating companies are expected to expand opportunities for technical verification and business cooperation by directly meeting local corporations, investors, and representatives from global Big Tech firms. The program is open to emerging tech companies located within the regional jurisdictions covered by the 4 ISTs. KAIST is in charge of the Central Region, GIST the Honam Region, DGIST the Daekyeong Region, and UNIST the Southeast Region. However, if the company representative belongs to one of the 4 ISTs, they can apply through their affiliated institution regardless of their physical company location. This lowers the barrier to entry for startup companies founded by researchers, faculty, and graduate students belonging to the ISTs.
Kwang-Hyung Lee, President of KAIST, stated, “By combining the cooperation between the ISTs and our regional technology startup networks, we will establish a practical global expansion support system for emerging tech startups,” adding, “We will actively support domestic tech companies to minimize trial and error in entering overseas markets, generate rapid outcomes, and grow into globally competitive enterprises.”
Meanwhile, the ‘2026 Emerging Tech Global Launchpad’ is a case where the scope of cooperation has been expanded to corporate-targeted overseas customer validation and PoC support, following the ‘Deep-Tech Student Startup Integrated League (GRAVITY)’ currently co-operated by the 4 ISTs. It holds great significance as it expands the scope of support beyond discovering student startups to facilitating the overseas verification and global market settlement of regionally-based emerging tech startups. Detailed recruitment schedules, support requirements, and application methods can be found on the official ‘Emerging Tech Global Launchpad’ website (https://launchpad2026.io/) and the KAIST Institute for Startup Advancement (https://startup.kaist.ac.kr/).
2026.06.01 View 576 -
Development of a Virtual AI Testbed Capable of Performance Verification Before Building Massive AI Servers
< From left: Professor Jongse Park , M.S candidate Jaehong Cho, M.S candidate Hyunmin Choi, Professor Brandon Reagen ISPASS >
Operating Large Language Model (LLM) services like ChatGPT requires a server infrastructure on the scale of tens of thousands of units. However, constructing actual equipment every time a new AI semiconductor or system architecture needs to be verified incurs massive costs and time. A research team at our university has developed a ‘virtual testbed’ that can pre-verify performance and efficiency inside a computer before building an actual large-scale AI server.
KAIST announced on May 29th that the research on a Large Language Model (LLM) serving infrastructure simulator (virtual testing software) developed by Professor Jongse Park’s research team in the School of Computing won the Best Paper Award at ‘ISPASS 2026 (IEEE International Symposium on Performance Analysis of Systems and Software),’ a world-renowned conference in the field of computer system performance analysis.
‘LLMServingSim 2.0,’ developed by the research team, is a simulation platform capable of virtually analyzing various hardware and software combinations in complex AI service environments. Researchers and developers can freely experiment with various design options and verify performance without having to directly build expensive, large-scale server infrastructures.
< LLMServingSim 2.0 is workload >
In particular, this technology is drawing attention because it goes beyond the existing Graphics Processing Unit (GPU)-centric environment to support diverse hardware environments, including Neural Processing Units (NPUs), which are rising as next-generation AI semiconductors, and Processing-In-Memory (PIM, a semiconductor technology that performs operations inside the memory).
In other words, it is a technology that allows future-oriented AI semiconductors that have not yet been commercialized to be tested in advance within a virtual datacenter environment. Through this, it is possible to replicate and analyze inside a computer how much the service speed improves, how much power consumption is reduced, and whether it operates stably even in a server environment scaled to tens of thousands of units when a specific semiconductor is applied.
In addition, it reproduces complex operations that occur during actual AI service operations—such as data processing, request distribution, and memory utilization—at the system level, enabling performance evaluations that are close to reality. Notably, it can even analyze disaggregated infrastructure environments where multiple server resources are separated and connected for use, showing great potential for utilization in next-generation AI datacenter research.
This simulator is expected to be widely utilized not only by researchers but also by LLM service companies and AI semiconductor startups to design and optimize next-generation AI infrastructures. This is because it can rapidly verify new AI semiconductors or service architectures prior to actual construction, thereby significantly reducing the cost and time of AI infrastructure development.
< Research Image (AI-generated image) >
Professor Jongse Park said, “The competitiveness of AI services is determined not only by the model itself but also by the infrastructure technology that operates it stably and efficiently.” He added, “We hope this simulator will serve as an important foundation for researchers and the industry to develop next-generation AI infrastructures faster and more efficiently.”
This research was led by M.S candidate Jaehong Cho and Hyunmin Choi in the School of Computing as co-first authors. Following their Best Paper Award at the 2024 IISWC (IEEE International Symposium on Workload Characterization), the research team won the Best Paper Award again at this ISPASS 2026, proving their research competitiveness in the field of AI infrastructure once more.
※ Paper Title: LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure, DOI: 10.1109/ISPASS69572.2026.00012 (Authors: Jaehong Cho, Hyunmin Choi, Guseul Heo, Jongse Park) ※ Open Source Link: https://llmservingsim.ai/ Meanwhile, this research was conducted with support from the Ministry of Science and ICT (MSIT), the Institute for Information & Communications Technology Planning & Evaluation (IITP, No. RS-2024-00396013), the Electronics and Telecommunications Research Institute (ETRI, No. RS-2025-02305453), and SK hynix.
2026.05.29 View 601 -
Clearing the Expressway for Bubble Blockages to Achieve High-Efficiency Green Hydrogen Production
< (From left) Ph.D candidate Jaeho Byeon, Ph.D candidate Minkyeong Ban, Professor Jinwoo Lee, Dr. Sungjun Kim, Professor Jang Yong Lee>
As the global transition toward carbon neutrality accelerates, "water electrolysis"—a technology that splits water electrically to produce clean hydrogen—is drawing significant attention. However, a major limitation has been the decline in efficiency caused by bubbles formed during the electrolysis process that block the pathways. A domestic research team has resolved this challenge by developing an innovative technology that rapidly discharges bubbles and boosts hydrogen production efficiency, much like clearing an expressway through a heavily congested road.
KAIST announced on May 28th that a research team led by Professor Jinwoo Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Dr. Sungjun Kim from KRICT (President Suk-min Shin) and a research team led by Professor Jang Yong Lee from Konkuk University (President Jong-phil Won), has departed from the conventional method of simply increasing catalytic activity itself. Instead, they have successfully secured both water electrolysis performance and stability simultaneously by newly designing a "pathway" inside the catalyst layer through which water and gas pass.
< Development of World-Class Anion Exchange Membrane Water Electrolysis via Carbon-Induced Ru-C Bonds and Catalyst Layer Structural Design >
Using paper-thin 2D mesoporous carbon (a thin carbon structure with numerous nanoscale pores) nanosheets, the research team created a low-tortuosity structure where materials can move without obstruction. Simply put, they implemented a "highway-like pathway" inside the catalyst layer through which water and gas can pass rapidly, instead of a narrow and complex alleyway.
Furthermore, ruthenium (Ru) nanoclusters (ultrafine metal particles several nanometers in size) were stably anchored onto the defect-introduced carbon surface to accelerate the hydrogen evolution reaction rate. Simultaneously, the interface structure was controlled to prevent catalyst degradation even during long-term operation.
Through this technology, it was confirmed that bubbles generated during the water electrolysis process were rapidly discharged without accumulating inside the catalyst layer, and a stable reaction was maintained even under extreme environments with high current density.
As a result, the technology recorded a world-class performance of 17.1 A cm⁻² at 80°C, vastly exceeding the 2026 target set by the U.S. Department of Energy (DOE). This figure represents the amount of current flowing per unit area; a higher value signifies that more hydrogen can be produced faster.
In addition, it demonstrated practical industrial applicability by operating stably for over 1,000 hours even under a low noble metal loading condition (0.09 mgRu cm⁻²). This means that the amount of ruthenium, a precious metal used in the catalyst, has been significantly reduced, which can also enhance the economic viability of water electrolysis systems.
The core of this research lies not simply in making a "good catalyst," but in newly designing the pathway itself through which hydrogen is formed. In conventional water electrolysis devices, bubbles generated during the reaction process accumulate inside, blocking the flow of water and electricity, which leads to a degradation in performance. The research team solved this problem by changing the structure of the catalyst layer so that bubbles can exit rapidly.
This technology holds great significance as it opens the way to produce eco-friendly hydrogen more affordably and efficiently in the future. Hydrogen is currently attracting attention as a core clean energy source for the carbon-neutral era, but it has faced limitations due to high production costs and low system efficiency. In particular, conventional high-performance water electrolysis devices required large amounts of expensive noble metals, making large-scale commercialization difficult.
The research team explained that this technology demonstrates the potential to achieve high performance and stability with only a small amount of noble metals. It is expected to expand into various fields in the future, including large-scale green hydrogen production, eco-friendly power generation systems, hydrogen vehicles/eco-friendly mobility, and carbon-neutral industrial processes.
< 2D Mesoporous Catalyst Layer-Based Green Hydrogen Production Technology (AI-Generated Image) >
Professor Jinwoo Lee stated, "This research is a technology that improves water electrolysis efficiency by designing not only the catalyst itself but also the path through which energy flows. Since high-efficiency green hydrogen production is possible with only a small amount of noble metals, we expect to accelerate the commercialization of eco-friendly hydrogen production in the future."
In this study, PhD students Jaeho Byeon and Minkyeong Ban from the KAIST Department of Chemical and Biomolecular Engineering participated as co-first authors. The research findings were published online on May 22, 2026, in Joule, the world's leading academic journal in the energy field, and will be featured in the formal issue of Joule on September 16.
※ Paper Title: Outperforming water electrolysis through catalyst layer structuring with defective 2D mesoporous carbon, DOI: 10.1016/j.joule.2026.102478
※ Author Information: A total of 18 authors including Jaeho Byeon (KAIST, co-first author), Minkyeong Ban (KAIST, co-first author), Liangliang Xu (co-first author), Seunggeon Lee, Seongbeen Kim, Seonggyu Lee, Seongmin Shin, Donghyeok Son, Wonchul Park, Jinkyu Park, Hoyoung Kim, Dongyoon Woo, Seongseop Kim, Dong Young Chung, Jaewook Nam, Jang Yong Lee (Konkuk University, corresponding author), Sungjun Kim (KRICT, corresponding author), and Jinwoo Lee (KAIST, corresponding author).
This research was conducted with support from the National Research Foundation of Korea’s "AEM Water Electrolysis Technology Development" (RS-2024-00467234), the "Nano-Future Materials Source Technology Development" (RS-2023-00235596), the Ministry of Education’s "Ph.D. Student Research Support Project" (RS-2025-25424765), the Korea Research Institute of Chemical Technology (KS2522-10), and the Lotte Chemical Carbon Neutral Center.
2026.05.28 View 639 -
KAIST Develops AI Technology That Automatically Generates Sounds as If a “Jurassic Park” Dinosaur Were Actually Walking Toward You
<(From Left) Hyun-Bin Oh, Takida Yuhta, Uesaka Toshimitsu, Tae-Hyun Oh, Mitsufuji Yuki>
When people watch a scene in the film Jurassic Park where a giant dinosaur walks toward them, they naturally imagine a heavy, rumbling sound, as if the ground were shaking. This is because humans predict sound by considering not only the shape of an object, but also physical properties such as its size, weight, and speed of movement. However, existing video-to-audio generation AI mainly generates sound based on the category of objects or scene information in the video, and has not sufficiently reflected physical properties that vary depending on weight or speed.
KAIST (President Kwang Hyung Lee) announced on the 26th of May that a collaborative research team involving Professor Tae-Hyun Oh of the School of Computing, KAIST, together with joint researchers from POSTECH (President Sung Keun Kim) and Sony AI, has developed “PAVAS (Physics-Aware Video-to-Audio Synthesis),” an artificial intelligence (AI) technology that understands the physical situation in a video and generates more realistic sound.
<Concept Diagram of PAVAS (Physics-Aware Video-to-Audio Synthesis) Technology>
The key feature of this technology is that it is designed so that AI can infer invisible physical information such as the mass and velocity of objects in a video on its own. Ordinary videos do not provide exact numerical values for an object’s weight or speed, but the research team enabled AI to estimate them by analyzing the surrounding environment and movement context, and to reflect the results in the sound generation process.
In other words, the AI was designed to go beyond simply recognizing “what is visible” and to understand the physical cause of “why this sound should occur.”
As a result of technical validation, the research team’s AI generated sounds very similar to real-world environments in scenes involving physical interactions such as collisions or impacts between objects. In particular, it produced more realistic audio in which loudness and tone naturally changed when the mass and velocity of objects varied.
Recently, generative AI technologies that simultaneously generate video and audio have been advancing rapidly. Representative examples include Google’s “Veo 3” and ByteDance’s “Seedance 2.0.” However, in actual film, advertising, and game production sites, there is far greater demand for post-production work that adds sound effects suited to existing video scenes or supplements audio than for generating entirely new videos.
While existing commercial AI models have focused on generating video and audio together, PAVAS is differentiated by its ability to analyze the movement and collision characteristics of objects in a video and generate realistic sound effects that precisely match the scene.
<Comparison of Spectrograms Generated by Conventional Video-to-Audio Models and PAVAS>
The research team explained that this technology presents new possibilities in the field of “Physical AI,” or physically consistent generative AI. Physically consistent generative AI refers to AI that goes beyond simply producing plausible results and understands the laws of physics and causal relationships in the real world.
In the future, this technology is expected to provide more immersive user experiences in a wide range of fields, including the automation of content sound production, augmented reality (AR) and virtual reality (VR) content, the metaverse, and robotics simulation.
Professor Tae-Hyun Oh stated, “While existing generative AI has developed by increasing the scale of data and models, this research is meaningful in that it was designed so that AI directly understands physical quantities and causal relationships,” adding, “In the future, it can be expanded into a core foundational technology for next-generation multimodal AI that simultaneously understands and processes diverse types of information, including text, video, and speech.”
This study was led by POSTECH integrated M.S.-Ph.D. student Hyun-Bin Oh as the first author, with KAIST Professor Tae-Hyun Oh and Sony AI researchers Yuhta Takida, Toshimitsu Uesaka, and Yuki Mitsufuji participating as co-authors. This research was selected as an Oral presentation paper at CVPR 2026 (Computer Vision and Pattern Recognition 2026), the world’s most prestigious academic conference in the field of computer vision (image-based artificial intelligence technology), where only the top 0.88% of all papers are selected for oral presentation, recognizing the excellence of the work. The presentation is scheduled to take place on June 6.
※ Paper title: “PAVAS: Physics-Aware Video-to-Audio Synthesis,” DOI: https://arxiv.org/abs/2512.08282
This research was supported by the Mid-Career Research Program under the Basic Research Program of the Ministry of Science and ICT, the Pioneer Research Program for Future Converging Technology of the Ministry of Science, ICT and Future Planning, the AGI Program of the Ministry of Science and ICT, and the KAIST InnoCORE Program.
2026.05.27 View 1003