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KAIST Develops AI to Easily Find Promising Materials That Capture Only CO₂ < Photo 1. (From left) Professor Jihan Kim, Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of the Department of Chemical and Biomolecular Engineering > In order to help prevent the climate crisis, actively reducing already-emitted CO₂ is essential. Accordingly, direct air capture (DAC) — a technology that directly extracts only CO₂ from the air — is gaining attention. However, effectively capturing pure CO₂ is not easy due to water vapor (H₂O) present in the air. KAIST researchers have successfully used AI-driven machine learning techniques to identify the most promising CO₂-capturing materials among metal-organic frameworks (MOFs), a key class of materials studied for this technology. KAIST (President Kwang Hyung Lee) announced on the 29th of June that a research team led by Professor Jihan Kim from the Department of Chemical and Biomolecular Engineering, in collaboration with a team at Imperial College London, has developed a machine-learning-based simulation method that can quickly and accurately screen MOFs best suited for atmospheric CO₂ capture. < Figure 1. Concept diagram of Direct Air Capture (DAC) technology and carbon capture using Metal-Organic Frameworks (MOFs). MOFs are promising porous materials capable of capturing carbon dioxide from the atmosphere, drawing attention as a core material for DAC technology. > To overcome the difficulty of discovering high-performance materials due to the complexity of structures and the limitations of predicting intermolecular interactions, the research team developed a machine learning force field (MLFF) capable of precisely predicting the interactions between CO₂, water (H₂O), and MOFs. This new method enables calculations of MOF adsorption properties with quantum-mechanics-level accuracy at vastly faster speeds than before. Using this system, the team screened over 8,000 experimentally synthesized MOF structures, identifying more than 100 promising candidates for CO₂ capture. Notably, this included new candidates that had not been uncovered by traditional force-field-based simulations. The team also analyzed the relationships between MOF chemical structure and adsorption performance, proposing seven key chemical features that will help in designing new materials for DAC. < Figure 2. Concept diagram of adsorption simulation using Machine Learning Force Field (MLFF). The developed MLFF is applicable to various MOF structures and allows for precise calculation of adsorption properties by predicting interaction energies during repetitive Widom insertion simulations. It is characterized by simultaneously achieving high accuracy and low computational cost compared to conventional classical force fields. > This research is recognized as a significant advance in the DAC field, greatly enhancing materials design and simulation by precisely predicting MOF-CO₂ and MOF-H₂O interactions. The results of this research, with Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of KAIST as co-first authors, were published in the international academic journal Matter on June 12. ※Paper Title: Accelerating CO₂ direct air capture screening for metal–organic frameworks with a transferable machine learning force field ※DOI: 10.1016/j.matt.2025.102203 This research was supported by the Saudi Aramco-KAIST CO₂ Management Center and the Ministry of Science and ICT's Global C.L.E.A.N. Project.
2025.06.29 View 4463
KAIST Research Team Develops Stretchable Microelectrodes Array for Organoid Signal Monitoring < Photo 1. (From top left) Professor Hyunjoo J. Lee, Dr. Mi-Young Son, Dr. Mi-Ok Lee(In the front row from left) Doctoral student Kiup Kim, Doctoral student Youngsun Lee > On January 14th, the KAIST research team led by Professor Hyunjoo J. Lee from the School of Electrical Engineering in collaboration with Dr. Mi-Young Son and Dr. Mi-Ok Lee at Korea Research Institute of Bioscience and Biotechnology (KRIBB) announced the development of a highly stretchable microelectrode array (sMEA) designed for non-invasive electrophysiological signal measurement of organoids. Organoids* are highly promising models for human biology and are expected to replace many animal experiments. Their potential applications include disease modeling, drug screening, and personalized medicine as they closely mimic the structure and function of humans. *Organoids: three-dimensional in vitro tissue models derived from human stem cells Despite these advantages, existing organoid research has primarily focused on genetic analysis, with limited studies on organoid functionality. For effective drug evaluation and precise biological research, technology that preserves the three-dimensional structure of organoids while enabling real-time monitoring of their functions is needed. However, it’s challenging to provide non-invasive ways to evaluate the functionalities without incurring damage to the tissues. This challenge is particularly significant for electrophysiological signal measurement in cardiac and brain organoids since the sensor needs to be in direct contact with organoids of varying size and irregular shape. Achieving tight contact between electrodes and the external surface of the organoids without damaging the organoids has been a persistent challenge. < Figure 1. Schematic image of highly stretchable MEA (sMEA) with protruding microelectrodes. > The KAIST research team developed a highly stretchable microelectrode array with a unique serpentine structure that contacts the surface of organoids in a highly conformal fashion. They successfully demonstrated real-time measurement and analysis of electrophysiological signals from two types of electrogenic organoids (heart and brain). By employing a micro-electromechanical system (MEMS)-based process, the team fabricated the serpentine-structured microelectrode array and used an electrochemical deposition process to develop PEDOT:PSS-based protruding microelectrodes. These innovations demonstrated exceptional stretchability and close surface adherence to various organoid sizes. The protruding microelectrodes improved contact between organoids and the electrodes, ensuring stable and reliable electrophysiological signal measurements with high signal-to-noise ratios (SNR). < Figure 2. Conceptual illustration, optical image, and fluorescence images of an organoid captured by the sMEA with protruding microelectrodes.> Using this technology, the team successfully monitored and analyzed electrophysiological signals from cardiac spheroids of various sizes, revealing three-dimensional signal propagation patterns and identifying changes in signal characteristics according to size. They also measured electrophysiological signals in midbrain organoids, demonstrating the versatility of the technology. Additionally, they monitored signal modulations induced by various drugs, showcasing the potential of this technology for drug screening applications. < Figure 3. SNR improvement effect by protruding PEDOT:PSS microelectrodes. > Prof. Hyunjoo Jenny Lee stated, “By integrating MEMS technology and electrochemical deposition techniques, we successfully developed a stretchable microelectrode array adaptable to organoids of diverse sizes and shapes. The high practicality is a major advantage of this system since the fabrication is based on semiconductor fabrication with high volume production, reliability, and accuracy. This technology that enables in situ, real-time analysis of states and functionalities of organoids will be a game changer in high-through drug screening.” This study led by Ph.D. candidate Kiup Kim from KAIST and Ph.D. candidate Youngsun Lee from KRIBB, with significant contributions from Dr. Kwang Bo Jung, was published online on December 15, 2024 in Advanced Materials (IF: 27.4). < Figure 4. Drug screening using cardiac spheroids and midbrain organoids.> This research was supported by a grant from 3D-TissueChip Based Drug Discovery Platform Technology Development Program (No. 20009209) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), by the Commercialization Promotion Agency for R&D Outcomes (COMPA) funded by the Ministry of Science and ICT (MSIT) (RS-2024-00415902), by the K-Brain Project of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2023-00262568), by BK21 FOUR (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE, No. 4120200113769), and by Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program (KGM4722432).
2025.01.14 View 8479
KAIST team develops smart immune system that can pin down on malignant tumors A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort. Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects. This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells. Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway. At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy. Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.” This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation. Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR. Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09 View 17513
Professor Heung-Sun Sim the MSIT Scientist of July Professor Heung-Sun Sim from the Department of Physics was selected as the Scientist of July by the Ministry of Science and ICT. Professor Sim was recognized for his research of the Kondo effect, which opened a novel way to engineer spin screening and entanglement by directly observing a quantum phenomenon known as a Kondo screening cloud. His research revealed that the cloud can mediate interactions between distant spins confined in quantum dots, which is a necessary protocol for semiconductor spin-based quantum information processing. This phenomenon is essentially a cloud that masks magnetic impurities in a material. It was known to exist but its spatial extension had never been observed, creating controversy over whether such an extension actually existed. The research was reported in Nature in March 2020. With this award, Professor Sim received 10 million KRW in prize money.
2021.07.12 View 11908
Repurposed Drugs Present New Strategy for Treating COVID-19 Virtual screening of 6,218 drugs and cell-based assays identifies best therapeutic medication candidates A joint research group from KAIST and Institut Pasteur Korea has identified repurposed drugs for COVID-19 treatment through virtual screening and cell-based assays. The research team suggested the strategy for virtual screening with greatly reduced false positives by incorporating pre-docking filtering based on shape similarity and post-docking filtering based on interaction similarity. This strategy will help develop therapeutic medications for COVID-19 and other antiviral diseases more rapidly. This study was reported at the Proceedings of the National Academy of Sciences of the United States of America (PNAS). Researchers screened 6,218 drugs from a collection of FDA-approved drugs or those under clinical trial and identified 38 potential repurposed drugs for COVID-19 with this strategy. Among them, seven compounds inhibited SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, showed anti-SARS-CoV-2 activity in human lung cells, Calu-3. Drug repurposing is a practical strategy for developing antiviral drugs in a short period of time, especially during a global pandemic. In many instances, drug repurposing starts with the virtual screening of approved drugs. However, the actual hit rate of virtual screening is low and most of the predicted drug candidates are false positives. The research group developed effective filtering algorithms before and after the docking simulations to improve the hit rates. In the pre-docking filtering process, compounds with similar shapes to the known active compounds for each target protein were selected and used for docking simulations. In the post-docking filtering process, the chemicals identified through their docking simulations were evaluated considering the docking energy and the similarity of the protein-ligand interactions with the known active compounds. The experimental results showed that the virtual screening strategy reached a high hit rate of 18.4%, leading to the identification of seven potential drugs out of the 38 drugs initially selected. “We plan to conduct further preclinical trials for optimizing drug concentrations as one of the three candidates didn’t resolve the toxicity issues in preclinical trials,” said Woo Dae Jang, one of the researchers from KAIST. “The most important part of this research is that we developed a platform technology that can rapidly identify novel compounds for COVID-19 treatment. If we use this technology, we will be able to quickly respond to new infectious diseases as well as variants of the coronavirus,” said Distinguished Professor Sang Yup Lee. This work was supported by the KAIST Mobile Clinic Module Project funded by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF). The National Culture Collection for Pathogens in Korea provided the SARS-CoV-2 (NCCP43326). -PublicationWoo Dae Jang, Sangeun Jeon, Seungtaek Kim, and Sang Yup Lee. Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay. Proc. Natl. Acad. Sci. U.S.A. (https://doi/org/10.1073/pnas.2024302118) -ProfileDistinguished Professor Sang Yup LeeMetabolic &Biomolecular Engineering National Research Laboratoryhttp://mbel.kaist.ac.kr Department of Chemical and Biomolecular EngineeringKAIST
2021.07.08 View 21807
Scientists Observe the Elusive Kondo Screening Cloud Scientists ended a 50-year quest by directly observing a quantum phenomenon An international research group of Professor Heung-Sun Sim has ended a 50-year quest by directly observing a quantum phenomenon known as a Kondo screening cloud. This research, published in Nature on March 11, opens a novel way to engineer spin screening and entanglement. According to the research, the cloud can mediate interactions between distant spins confined in quantum dots, which is a necessary protocol for semiconductor spin-based quantum information processing. This spin-spin interaction mediated by the Kondo cloud is unique since both its strength and sign (two spins favor either parallel or anti-parallel configuration) are electrically tunable, while conventional schemes cannot reverse the sign. This phenomenon, which is important for many physical phenomena such as dilute magnetic impurities and spin glasses, is essentially a cloud that masks magnetic impurities in a material. It was known to exist but its spatial extension had never been observed, creating controversy over whether such an extension actually existed. Magnetism arises from a property of electrons known as spin, meaning that they have angular momentum aligned in one of either two directions, conventionally known as up and down. However, due to a phenomenon known as the Kondo effect, the spins of conduction electrons—the electrons that flow freely in a material—become entangled with a localized magnetic impurity, and effectively screen it. The strength of this spin coupling, calibrated as a temperature, is known as the Kondo temperature. The size of the cloud is another important parameter for a material containing multiple magnetic impurities because the spins in the cloud couple with one another and mediate the coupling between magnetic impurities when the clouds overlap. This happens in various materials such as Kondo lattices, spin glasses, and high temperature superconductors. Although the Kondo effect for a single magnetic impurity is now a text-book subject in many-body physics, detection of its key object, the Kondo cloud and its length, has remained elusive despite many attempts during the past five decades. Experiments using nuclear magnetic resonance or scanning tunneling microscopy, two common methods for understanding the structure of matter, have either shown no signature of the cloud, or demonstrated a signature only at a very short distance, less than 1 nanometer, so much shorter than the predicted cloud size, which was in the micron range. In the present study, the authors observed a Kondo screening cloud formed by an impurity defined as a localized electron spin in a quantum dot—a type of “artificial atom”—coupled to quasi-one-dimensional conduction electrons, and then used an interferometer to measure changes in the Kondo temperature, allowing them to investigate the presence of a cloud at the interferometer end. Essentially, they slightly perturbed the conduction electrons at a location away from the quantum dot using an electrostatic gate. The wave of conducting electrons scattered by this perturbation returned back to the quantum dot and interfered with itself. This is similar to how a wave on a water surface being scattered by a wall forms a stripe pattern. The Kondo cloud is a quantum mechanical object which acts to preserve the wave nature of electrons inside the cloud. Even though there is no direct electrostatic influence of the perturbation on the quantum dot, this interference modifies the Kondo signature measured by electron conductance through the quantum dot if the perturbation is present inside the cloud. In the study, the researchers found that the length as well as the shape of the cloud is universally scaled by the inverse of the Kondo temperature, and that the cloud’s size and shape were in good agreement with theoretical calculations. Professor Sim at the Department of Physics proposed the method for detecting the Kondo cloud in the co-research with the RIKEN Center for Emergent Matter Science, the City University of Hong Kong, the University of Tokyo, and Ruhr University Bochum in Germany. Professor Sim said, “The observed spin cloud is a micrometer-size object that has quantum mechanical wave nature and entanglement. This is why the spin cloud has not been observed despite a long search. It is remarkable in a fundamental and technical point of view that such a large quantum object can now be created, controlled, and detected. Dr. Michihisa Yamamoto of the RIKEN Center for Emergent Matter Science also said, “It is very satisfying to have been able to obtain real space image of the Kondo cloud, as it is a real breakthrough for understanding various systems containing multiple magnetic impurities. The size of the Kondo cloud in semiconductors was found to be much larger than the typical size of semiconductor devices.” Publication: Borzenets et al. (2020) Observation of the Kondo screening cloud. Nature, 579. pp.210-213. Available online at https://doi.org/10.1038/s41586-020-2058-6 Profile: Heung-Sun Sim, PhD Professor hssim@kaist.ac.kr https://qet.kaist.ac.kr/ Quantum Electron Correlation & Transport Theory Group (QECT Lab) https://qc.kaist.ac.kr/index.php/group1/ Center for Quantum Coherence In COndensed Matter Department of Physics https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
2020.03.13 View 23290
On-chip Drug Screening for Identifying Antibiotic Interactions in Eight Hours (from left: Seunggyu Kimand Professor Jessie Sungyun Jeon) A KAIST research team developed a microfluidic-based drug screening chip that identifies synergistic interactions between two antibiotics in eight hours. This chip can be a cell-based drug screening platform for exploring critical pharmacological patterns of antibiotic interactions, along with potential applications in screening other cell-type agents and guidance for clinical therapies. Antibiotic susceptibility testing, which determines types and doses of antibiotics that can effectively inhibit bacterial growth, has become more critical in recent years with the emergence of antibiotic-resistant pathogenic bacteria strains. To overcome the antibiotic-resistant bacteria, combinatory therapy using two or more kinds of antibiotics has been gaining considerable attention. However, the major problem is that this therapy is not always effective; occasionally, unfavorable antibiotic pairs may worsen results, leading to suppressed antimicrobial effects. Therefore, combinatory testing is a crucial preliminary process to find suitable antibiotic pairs and their concentration range against unknown pathogens, but the conventional testing methods are inconvenient for concentration dilution and sample preparation, and they take more than 24 hours to produce the results. To reduce time and enhance the efficiency of combinatory testing, Professor Jessie Sungyun Jeon from the Department of Mechanical Engineering, in collaboration with Professor Hyun Jung Chung from the Department of Biological Sciences, developed a high-throughput drug screening chip that generates 121 pairwise concentrations between two antibiotics. The team utilized a microfluidic chip with a sample volume of a few tens of microliters. This chip enabled 121 pairwise concentrations of two antibiotics to be automatically formed in only 35 minutes. They loaded a mixture of bacterial samples and agarose into the microchannel and injected reagents with or without antibiotics into the surrounding microchannel. The diffusion of antibiotic molecules from the channel with antibiotics to the one without antibiotics resulted in the formation of two orthogonal concentration gradients of the two antibiotics on the bacteria-trapping agarose gel. The team observed the inhibition of bacterial growth by the antibiotic orthogonal gradients over six hours with a microscope, and confirmed different patterns of antibiotic pairs, classifying the interaction types into either synergy or antagonism. Professor Jeon said, “The feasibility of microfluidic-based drug screening chips is promising, and we expect our microfluidic chip to be commercialized and utilized in near future.” This study, led by Seunggyu Kim, was published in Lab on a Chip (10.1039/c8lc01406j) on March 21, 2019. Figure 1. Back cover image for the “Lab on a Chip”. Figure 2. Examples of testing results using the microfluidic chips developed in this research.
2019.04.18 View 34286