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KAIST Surpasses the Limits of AlphaFold… AI Now Predicts Whether Drugs Actually Work
<(From Left) Ph.D candidate Hyojin Son, Professor Gwan-su Yi>
Proteins in our body function like switches. When a drug binds to a protein, the structure at the binding site changes, and this structural change propagates throughout the protein, turning its function on or off. Google DeepMind’s AlphaFold3 successfully predicted whether drugs bind to proteins and the three-dimensional structure of binding sites. However, it could not predict how signals propagate inside the protein after drug binding, how the entire structure changes, or whether the protein’s function is ultimately activated or inhibited. KAIST researchers have developed an AI that predicts not only whether a drug binds but whether it actually works.
KAIST (President Kwang Hyung Lee) announced on the 4th of March that a research team led by Professor Gwan-Su Yi of the Department of Bio and Brain Engineering has developed an artificial intelligence model called “GPCRact” that predicts whether candidate molecules not only bind to G-protein-coupled receptors (GPCRs)—a major drug target—but also actually activate the protein.
GPCRs act as “signal receivers” on the surface of cells. When hormones, neurotransmitters, or drugs send signals from outside the cell, GPCRs function as gates that receive these signals and transmit them into the cell. There are about 800 types of GPCRs in the human body, and roughly 30–40% of currently marketed drugs target them. They are key proteins involved in numerous physiological functions, including heart rate regulation, blood pressure control, pain sensing, immune responses, and emotional regulation.
However, a drug binding to a GPCR does not always trigger the desired biological function. Structural changes inside the protein and subsequent signal transmission determine whether the drug actually produces an effect. This process is known as allosteric signal propagation.
The research team designed the AI to learn the drug action process in two stages: ① the drug–target binding stage, and ② the intracellular signal propagation stage within the protein. The three-dimensional protein structure was represented as an atom-level graph, and an attention mechanism was applied to enable the model to learn important signaling pathways. Through this approach, the AI analyzes not only the drug binding signal but also the internal signaling pathways of the protein to predict whether the protein becomes activated.
As a result, the model significantly improved the prediction performance of drug activity even in proteins with complex structures that existing models struggled to analyze. Importantly, the model does not simply output “active” or “inactive.” It also presents the key internal signaling pathways that form the basis of its predictions, overcoming the limitations of so-called “black-box AI.”
<Schematic diagram of drug activity prediction and mechanism interpretation using the GPCRact artificial intelligence model>
This represents an important advance, as it allows researchers to interpret and verify predictions while simultaneously improving the reliability and efficiency of drug discovery. In the future, the model is expected to serve as a precision drug discovery AI platform capable of predicting not only whether drugs bind to GPCRs but also whether they truly activate them in various diseases targeting GPCRs.
<AI-generated image to help illustrate the research>
Professor Gwan-Su Yi explained, “Allosteric structural change refers to a phenomenon in which a drug binds to one part of a protein and its influence propagates internally, altering the function of other regions,” adding, “The key contribution of this research is incorporating this operational principle into deep learning.” He further noted, “We plan to expand the model to various proteins and ultimately develop technologies capable of predicting cellular and whole-body responses.”
Ph.D candidate Hyojin Son participated as the first author in this study. The paper was published on January 15 in the international journal Briefings in Bioinformatics, one of the leading journals in the field of bioinformatics.
※ Paper title: “GPCRact: a hierarchical framework for predicting ligand-induced GPCR activity via allosteric communication modeling”
DOI: https://doi.org/10.1093/bib/bbaf719※ Author information: Hyojin Son (KAIST, first author), Gwan-Su Yi (KAIST, corresponding author)
This research was supported by the Basic Research Program for Individual Research funded by the Ministry of Science and ICT and the National Research Foundation of Korea (RS-2025-24533057).
2026.03.09 View 1263 -
AI that Understands Chemical Principles... Accelerating the Development of New Drugs and Materials
<(From top left) Professor Woo Youn Kim (KAIST), Dr. Jeheon Woo (KISTI), Dr. Seonghwan Kim (KAIST), and Jun Hyeong Kim (PhD candidate)>
Whether a smartphone battery lasts longer or a new drug can be developed to treat incurable diseases depends on how stably the atoms constituting the material are bonded. The core of 'molecular design' lies in finding how to arrange these countless atoms to form the most stable molecule. Until now, this process has been as difficult as finding the lowest valley in a massive mountain range, requiring immense time and costs. Researchers at KAIST have developed a new technology that uses artificial intelligence to solve this process quickly and accurately.
KAIST announced on February 10th that Professor Woo Youn Kim's research team in the Department of Chemistry has developed 'Riemannian DenoisingModel (R-DM),' an artificial intelligence model that understands the physical laws governing molecular stability to predict structures.
The most significant feature of this model is that it directly considers the 'energy' of the molecule. While existing AI models simply mimicked the shape of molecules, R-DM refines the structure by considering the forces acting within the molecule. The research team represented the molecular structure as a map where higher energy is depicted as hills and lower energy as valleys, designing the AI to move toward and find the valleys with the lowest energy.
R-DM completes the molecule by navigating this energy landscape, avoiding unstable structures to find the most stable state. This applies the mathematical theory of 'Riemannian geometry,' resulting in the AI learning the fundamental law of chemistry: 'matter prefers the state with the lowest energy.'
Experimental results showed that R-DM achieved up to 20 times higher accuracy than existing AI models, reducing prediction errors to a level nearly indistinguishable from precise quantum mechanical calculations. This represents the world's highest level of performance among AI-based molecular structure prediction technologies.
<Comparison of energy landscapes in Euclidean space and Riemannian space>
This technology can be utilized in various fields, including new drug development, next-generation battery materials, and high-performance catalyst design. It is expected to serve as an 'AI simulator' that will dramatically speed up research and development by significantly shortening the molecular design process, which previously took a long time. Furthermore, it has great potential in environmental and safety fields, as it can quickly predict chemical reaction paths in situations where experiments are difficult, such as chemical accidents or the spread of hazardous substances.
Professor Woo Youn Kim stated, "This is the first case where artificial intelligence has understood the basic principles of chemistry and judged molecular stability on its own. It is a technology that can fundamentally change the way new materials are developed."
<Image of Riemannian Diffusion Model application (AI-generated image)>
This study was led by Dr. Jeheon Woo from the KISTI Supercomputing Center and Dr. Seonghwan Kim from the KAIST Innovative Drug Discovery Research Group as co-first authors. The research results were published on January 2nd in the world-renowned academic journal Nature Computational Science.
※ Paper Title: Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy, DOI: 10.1038/s43588-025-00919-1
Meanwhile, this research was conducted with the support of the Chemical Accident Prediction-Prevention Advanced Technology Development Project of the Korea Environmental Industry & Technology Institute, the Science and Technology Institute InnoCore Project of the Ministry of Science and ICT, and the Data Science Convergence Talent Cultivation Project conducted by the National Research Foundation of Korea with support from the Ministry of Science and ICT.
2026.02.10 View 2212 -
KAIST Develops AI Technology That Predicts and Assembles Cell Drug Responses Like Lego Blocks
<(From left) Dr. Younghyun Han, (top center) Dr. Chun-Kyung Lee, (bottom center) Prof. Kwang-Hyun Cho,Ph.D. candidate Hyunjin Kim>
Controlling the state of a cell in a desired direction is one of the central challenges in life sciences, including drug development, cancer treatment, and regenerative medicine. However, identifying the right drug or genetic target for that purpose is extremely difficult. To address this, researchers at KAIST have mathematically modeled the interaction between cells and drugs in a modular “Lego block” manner—breaking them down and recombining them—to develop a new AI technology that can predict not only new cell–drug reactions never before tested but also the effects of arbitrary genetic perturbations.
KAIST (President Kwang Hyung Lee) announced on the 16th of October that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has developed a generative AI-based technology capable of identifying drugs and genetic targets that can guide cells toward a desired state.
“Latent space” is an invisible mathematical map used by image-generating AI to organize the essential features of objects or cells. The research team succeeded in separating the representations of cell states and drug effects within this space and then recombining them to predict the reactions of previously untested cell–drug combinations. They further extended this principle to show that the model can also predict how a cell’s state would change when a specific gene is regulated.
The team validated this approach using real experimental data. As a result, the AI identified molecular targets capable of reverting colorectal cancer cells toward a normal-like state, which the team later confirmed through cell experiments.
This finding demonstrates that the method is not limited to cancer treatment—it serves as a general platform capable of predicting various untrained cell-state transitions and drug responses. In other words, the technology not only determines whether or not a drug works but also reveals how it functions inside the cell, making the achievement particularly meaningful.
<Latent Space Direction Vector–Based Cell Transition Modeling>
The research provides a powerful tool for designing methods to induce desired cell-state changes. It is expected to have broad applications in drug discovery, cancer therapy, and regenerative medicine, such as restoring damaged cells to a healthy state.
Professor Kwang-Hyun Cho stated, “Inspired by image-generation AI, we applied the concept of a ‘direction vector,’ an idea that allows us to transform cells in a desired direction.” He added, “This technology enables quantitative analysis of how specific drugs or genes affect cells and even predicts previously unknown reactions, making it a highly generalizable AI framework.”
The study was conducted with Dr. Younghyun Han, Ph.D. candidate Hyunjin Kim, and Dr. Chun-Kyung Lee of KAIST. The research findings were published online in Cell Systems, a journal by Cell Press, on October 15.
※ Paper title: “Identifying an Optimal Perturbation to Induce a Desired Cell State by Generative Deep Learning” (DOI: 10.1016/j.cels.2025.101405)
The study was supported by the National Research Foundation of Korea (NRF) through the Ministry of Science and ICT’s Mid-Career Researcher Program and the Basic Research Laboratory (BRL) Program.
2025.10.16 View 3600 -
KAIST Develops AI That Automatically Designs Optimal Drug Candidates for Cancer-Targeting Mutations
< (From left) Ph.D candidate Wonho Zhung, Ph.D cadidate Joongwon Lee , Prof. Woo Young Kim , Ph.D candidate Jisu Seo >
Traditional drug development methods involve identifying a target protin (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.
KAIST (President Kwang Hyung Lee) announced on the 10th that a 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.
The core innovation of this technology lies in its “simultaneous design” approach. Previous AI models either focused on generating molecules or separately evaluating whether the generated molecule could bind to the target protein. In contrast, this new model considers the binding mechanism between the molecule and the protein during the generation process, enabling comprehensive design in one step. Since it pre-accounts for critical factors in protein-ligand binding, it has a much higher likelihood of generating effective and stable molecules. The generation process visually demonstrates how types and positions of atoms, covalent bonds, and interactions are created simultaneously to fit the protein’s binding site.
<Figure 1. Schematic of the diffusion model developed by the research team, which generates molecular structures and non-covalent interactions based on protein structures. Starting from a noise distribution, the model gradually removes noise (via reverse diffusion) to restore the atom positions, types, covalent bond types, and interaction types, thereby generating molecules. Interacting patterns are extracted from prior knowledge of known binding molecules or proteins, and through an inpainting technique, these patterns are kept fixed during the reverse diffusion process to guide the molecular generation.>
Moreover, this model is designed to meet multiple essential drug design criteria simultaneously—such as target binding affinity, drug-like properties, and structural stability. Traditional models often optimized for only one or two goals at the expense of others, but this new model balances various objectives, significantly enhancing its practical applicability.
The research team explained that the AI operates based on a “diffusion model”—a generative approach where a structure becomes increasingly refined from a random state. This is the same type of model used in AlphaFold 3, the 2024 Nobel Chemistry Prize-winning tool for protein-ligand structure generation, which has already demonstrated high efficiency.
Unlike AlphaFold 3, which provides spatial coordinates for atom positions, this study introduced a knowledge-based guide grounded in actual chemical laws—such as bond lengths and protein-ligand distances—enabling more chemically realistic structure generation.
<Figure 2. (Left) Target protein and the original bound molecule; (Right) Examples of molecules designed using the model developed in this study. The values for protein binding affinity (Vina), drug-likeness (QED), and synthetic accessibility (SA) are shown at the bottom.>
Additionally, the team applied an optimization strategy where outstanding binding patterns from prior results are reused. This allowed the model to generate even better drug candidates without additional training. Notably, the AI successfully produced molecules that selectively bind to the mutated residues of EGFR, a cancer-related target protein.
This study is also meaningful because it advances beyond the team’s previous research, which required prior input about the molecular conditions for the interaction pattern of protein binding.
Professor Woo Youn Kim commented that “the newly developed AI can learn and understand the key features required for strong binding to a target protein, and design optimal drug candidate molecules—even without any prior input. This could significantly shift the paradigm of drug development.” He added, “Since this technology generates molecular structures based on principles of chemical interactions, it is expected to enable faster and more reliable drug development.”
Joongwon Lee and Wonho Zhung, PhD students in the Department of Chemistry, participated as co-first authors of this study. The research results were published in the international journal Advanced Science (IF = 14.1) on July 11.
● Paper Title: BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design
● DOI: 10.1002/advs.202502702
This research was supported by the National Research Foundation of Korea and the Ministry of Health and Welfare.
2025.08.12 View 5723 -
KAIST Presents a Breakthrough in Overcoming Drug Resistance in Cancer – Hope for Treating Intractable Diseases like Diabetes
<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>
One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.
On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.
Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.
< Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>
The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations* on all of the metabolic genes and analyzed the results.
*Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.
As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.
The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.
Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.
Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”
Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”
The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.
※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.
This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).
2025.07.08 View 5988 -
“One Experiment Is All It Takes”: KAIST Team Revolutionizes Drug Interaction Testing, Replacing 60,000 Studies
A groundbreaking new method developed by researchers at KAIST and Chungnam National University could drastically streamline drug interaction testing — replacing dozens of traditional experiments with just one.
The research, led by Professor Jae Kyoung Kim of KAIST Department of Mathematical Sciences & IBS Biomedical Mathematics Group and Professor Sang Kyum Kim of Chungnam National University's College of Pharmacy, introduces a novel analysis technique called 50-BOA, published in Nature Communications on June 5, 2025.
< Photo 1. (From left) Professor Sang Kyum Kim (Chungnam National University College of Pharmacy, co-corresponding author), Dr. Yun Min Song (IBS Biomedical Mathematics Group, formerly KAIST Department of Mathematical Sciences, co-first author), undergraduate student Hyeong Jun Jang (KAIST, co-first author), Professor Jae Kyoung Kim (KAIST and IBS Biomedical Mathematics Group, co-corresponding author) (Top left in the bubble) Professor Hwi-yeol Yun (Chungnam National University College of Pharmacy, co-author) >
For decades, scientists have had to repeat drug inhibition experiments across a wide range of concentrations to estimate inhibition constants — a process seen in over 60,000 scientific publications. But the KAIST-led team discovered that a single, well-chosen inhibitor concentration can yield even more accurate results.
< Figure 1. Graphical summary of 50-BOA. 50-BOA improves the accuracy and efficiency of inhibition constant estimation by using only a single inhibitor concentration instead of the traditionally used method of employing multiple inhibitor concentrations. >
“This approach challenges long-standing assumptions in experimental pharmacology,” says Prof. Kim. “It shows how mathematics can fundamentally redesign life science experiments.”
By mathematically analyzing the sources of error in conventional methods, the team found that over half the data typically collected adds no value or even skews results. Their new method not only cuts experimental effort by over 75%, but also enhances reproducibility and accuracy.
To help researchers adopt the method quickly, the team developed a user-friendly tool that takes simple Excel files as input, now freely available on GitHub:
☞ https://github.com/Mathbiomed/50-BOA
< Figure 2. The MATLAB and R package of 50-BOA at GitHub >
The work holds promise for faster and more reliable drug development, especially in assessing potential interactions in combination therapies. The U.S. FDA already emphasizes the importance of accurate enzyme inhibition assessment during early-stage drug evaluation — and this method could soon become a new gold standard.
2025.06.16 View 8839 -
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 9391 -
KAIST to Collaborate with AT&C to Take Dominance over Dementia
< Photo 1. (From left) KAIST Dean of the College of Natural Sciences Daesoo Kim, KAIST President Kwang Hyung Lee, AT&C Chairman Ki Tae Lee, AT&C CEO Jong-won Lee >
KAIST (President Kwang Hyung Lee) announced on January 9th that it signed a memorandum of understanding for a comprehensive mutual cooperation with AT&C (CEO Jong-won Lee) at its Seoul Dogok Campus to expand research investment and industry-academia cooperation in preparation for the future cutting-edge digital bio era.
Senile dementia is a rapidly increasing brain disease that affects 10% of the elderly population aged 65 and older, and approximately 38% of those aged 85 and older suffer from dementia. Alzheimer's disease is the most common dementia in the elderly and its prevalence has been increasing rapidly in the population of over 40 years of age. However, an effective treatment is yet to be found.
The Korean government is investing a total of KRW 1.1 trillion in dementia R&D projects from 2020 to 2029, with the goal of reducing the rate of increase of dementia patients by 50%. Since it takes a lot of time and money to develop effective and affordable medicinal dementia treatments, it is urgent to work on the development of digital treatments for dementia that can be applied more quickly.
AT&C, a digital healthcare company, has already received approval from the Ministry of Food and Drug Safety (MFDS) for its device for antidepressant treatment based on transcranial magnetic stimulation (TMS) using magnetic fields and is selling it domestically and internationally. In addition, it has developed the first Alzheimer's dementia treatment device in Korea and received MFDS approval for clinical trials. After passing phase 1 to evaluate safety and phase 2 to test efficacy on some patients, it is currently conducting phase 3 clinical trials to test efficacy on a larger group of patients.
This dementia treatment device is equipped with a system that combines non-invasive electronic stimulations (TMS electromagnetic stimulator) and digital therapeutic prescription (cognitive learning programs) to provide precise, automated treatment by applying AI image analysis and robotics technology.
Through this agreement, KAIST and AT&C have agreed to cooperate with each other in the development of innovative digital treatment equipment for brain diseases. Through research collaboration with KAIST, AT&C will be able to develop technology that can be widely applied to Parkinson's disease, stroke, mild cognitive impairment, sleep disorders, etc., and will develop portable equipment that can improve brain function and prevent dementia at home by utilizing KAIST's wearable technology.
To this end, AT&C plans to establish a digital healthcare research center at KAIST by supporting research personnel and research expenses worth approximately 3 billion won with the goal of developing cutting-edge digital equipment within 3 years.
The digital equipment market is expected to grow at a compounded annual growth rate of 22.1% from 2023 to 2033, reaching a market size of $1.9209 trillion by 2033.
< Photo 2. (From left) Dean of the KAIST College of Natural Sciences Daesoo Kim, Professor Young-joon Lee, Professor Minee Choi of the KAIST Department of Brain and Cognitive Sciences, KAIST President Kwang Hyung Lee, Chairman Ki Tae Lee, CEO Jong-won Lee, and Headquarters Director Ki-yong Na of AT&C >
CEO Jong-won Lee said, “AT&C is playing a leading role in the treatment of Alzheimer’s disease using TMS (transcranial magnetic stimulation) technology. Through this agreement with KAIST, we will do our best to create a new paradigm for brain disease treatment and become a platform company that can lead future medical devices and medical technology.”
Former Samsung Electronics Vice Chairman Ki Tae Lee, a strong supporter of this R&D project, said, “Through this agreement with KAIST, we plan to prepare for a new future by combining the technologies AT&C has developed so far with KAIST’s innovative and differentiated technologies.”
KAIST President Kwang Hyung Lee emphasized, “Through this collaboration, KAIST expects to build a world-class digital therapeutics infrastructure for treating brain diseases and contribute greatly to further strengthening Korea’s competitiveness in the biomedical field.”
The signing ceremony was attended by KAIST President Kwang Hyung Lee, the Dean of KAIST College of Natural Sciences Daesoo Kim, AT&C CEO Lee Jong-won, and the current Chairman of AT&C, Ki Tae Lee, former Vice Chairman of Samsung Electronics.
2025.01.09 View 11069 -
KAIST Changes the Paradigm of Drug Discovery with World's First Atomic Editing
In pioneering drug development, the new technology that enables the easy and rapid editing of key atoms responsible for drug efficacy has been regarded as a fundamental and "dream" technology, revolutionizing the process of discovering potential drug candidates. KAIST researchers have become the first in the world to successfully develop single-atom editing technology that maximizes drug efficacy.
On October 8th, KAIST (represented by President Kwang-Hyung Lee) announced that Professor Yoonsu Park’s research team from the Department of Chemistry successfully developed technology that enables the easy editing and correction of oxygen atoms in furan compounds into nitrogen atoms, directly converting them into pyrrole frameworks, which are widely used in pharmaceuticals.
< Image. Conceptual image illustrating the main idea of the research >
This research was published in the prestigious scientific journal Science on October 3rd under the title "Photocatalytic Furan-to-Pyrrole Conversion."
Many drugs have complex chemical structures, but their efficacy is often determined by a single critical atom. Atoms like oxygen and nitrogen play a central role in enhancing the pharmacological effects of these drugs, particularly against viruses.
This phenomenon, where the introduction of specific atoms into a drug molecule dramatically affects its efficacy, is known as the "Single Atom Effect." In leading-edge drug development, discovering atoms that maximize drug efficacy is key.
However, evaluating the Single Atom Effect has traditionally required multi-step, costly synthesis processes, as it has been difficult to selectively edit single atoms within stable ring structures containing oxygen or nitrogen.
Professor Park’s team overcame this challenge by introducing a photocatalyst that uses light energy. They developed a photocatalyst that acts as a “molecular scissor,” freely cutting and attaching five-membered rings, enabling single-atom editing at room temperature and atmospheric pressure—a world first.
The team discovered a new reaction mechanism in which the excited molecular scissor removes oxygen from furan via single-electron oxidation and then sequentially adds a nitrogen atom.
Donghyeon Kim and Jaehyun You, the study's first authors and candidates in KAIST’s integrated master's and doctoral program in the Department of Chemistry, explained that this technique offers high versatility by utilizing light energy to replace harsh conditions. They further noted that the technology enables selective editing, even when applied to complex natural products or pharmaceuticals. Professor Yoonsu Park, who led the research, remarked, "This breakthrough, which allows for the selective editing of five-membered organic ring structures, will open new doors for building libraries of drug candidates, a key challenge in pharmaceuticals. I hope this foundational technology will be used to revolutionize the drug development process."
The significance of this research was highlighted in the Perspective section of Science, a feature where a peer scientist of prominence outside of the project group provides commentary on an impactful research.
This research was supported by the National Research Foundation of Korea’s Creative Research Program, the Cross-Generation Collaborative Lab Project at KAIST, and the POSCO Science Fellowship of the POSCO TJ Park Foundation.
2024.10.11 View 11127 -
KAIST leads AI-based analysis on drug-drug interactions involving Paxlovid
KAIST (President Kwang Hyung Lee) announced on the 16th that an advanced AI-based drug interaction prediction technology developed by the Distinguished Professor Sang Yup Lee's research team in the Department of Biochemical Engineering that analyzed the interaction between the PaxlovidTM ingredients that are used as COVID-19 treatment and other prescription drugs was published as a thesis. This paper was published in the online edition of 「Proceedings of the National Academy of Sciences of America」 (PNAS), an internationally renowned academic journal, on the 13th of March.
* Thesis Title: Computational prediction of interactions between Paxlovid and prescription drugs (Authored by Yeji Kim (KAIST, co-first author), Jae Yong Ryu (Duksung Women's University, co-first author), Hyun Uk Kim (KAIST, co-first author), and Sang Yup Lee (KAIST, corresponding author))
In this study, the research team developed DeepDDI2, an advanced version of DeepDDI, an AI-based drug interaction prediction model they developed in 2018. DeepDDI2 is able to compute for and process a total of 113 drug-drug interaction (DDI) types, more than the 86 DDI types covered by the existing DeepDDI.
The research team used DeepDDI2 to predict possible interactions between the ingredients (ritonavir, nirmatrelvir) of Paxlovid*, a COVID-19 treatment, and other prescription drugs. The research team said that while among COVID-19 patients, high-risk patients with chronic diseases such as high blood pressure and diabetes are likely to be taking other drugs, drug-drug interactions and adverse drug reactions for Paxlovid have not been sufficiently analyzed, yet. This study was pursued in light of seeing how continued usage of the drug may lead to serious and unwanted complications.
* Paxlovid: Paxlovid is a COVID-19 treatment developed by Pfizer, an American pharmaceutical company, and received emergency use approval (EUA) from the US Food and Drug Administration (FDA) in December 2021.
The research team used DeepDDI2 to predict how Paxrovid's components, ritonavir and nirmatrelvir, would interact with 2,248 prescription drugs. As a result of the prediction, ritonavir was predicted to interact with 1,403 prescription drugs and nirmatrelvir with 673 drugs.
Using the prediction results, the research team proposed alternative drugs with the same mechanism but low drug interaction potential for prescription drugs with high adverse drug events (ADEs). Accordingly, 124 alternative drugs that could reduce the possible adverse DDI with ritonavir and 239 alternative drugs for nirmatrelvir were identified.
Through this research achievement, it became possible to use an deep learning technology to accurately predict drug-drug interactions (DDIs), and this is expected to play an important role in the digital healthcare, precision medicine and pharmaceutical industries by providing useful information in the process of developing new drugs and making prescriptions.
Distinguished Professor Sang Yup Lee said, "The results of this study are meaningful at times like when we would have to resort to using drugs that are developed in a hurry in the face of an urgent situations like the COVID-19 pandemic, that it is now possible to identify and take necessary actions against adverse drug reactions caused by drug-drug interactions very quickly.”
This research was carried out with the support of the KAIST New-Deal Project for COVID-19 Science and Technology and the Bio·Medical Technology Development Project supported by the Ministry of Science and ICT.
Figure 1. Results of drug interaction prediction between Paxlovid ingredients and representative approved drugs using DeepDDI2
2023.03.16 View 15221 -
Scientists re-writes FDA-recommended equation to improve estimation of drug-drug interaction
Drugs absorbed into the body are metabolized and thus removed by enzymes from several organs like the liver. How fast a drug is cleared out of the system can be affected by other drugs that are taken together because added substance can increase the amount of enzyme secretion in the body. This dramatically decreases the concentration of a drug, reducing its efficacy, often leading to the failure of having any effect at all. Therefore, accurately predicting the clearance rate in the presence of drug-drug interaction* is critical in the process of drug prescription and development of a new drug in order to ensure its efficacy and/or to avoid unwanted side-effects.
*Drug-drug interaction: In terms of metabolism, drug-drug interaction is a phenomenon in which one drug changes the metabolism of another drug to promote or inhibit its excretion from the body when two or more drugs are taken together. As a result, it increases the toxicity of medicines or causes loss of efficacy.
Since it is practically impossible to evaluate all interactions between new drug candidates and all marketed drugs during the development process, the FDA recommends indirect evaluation of drug interactions using a formula suggested in their guidance, first published in 1997, revised in January of 2020, in order to evaluate drug interactions and minimize side effects of having to use more than one type of drugs at once.
The formula relies on the 110-year-old Michaelis-Menten (MM) model, which has a fundamental limit of making a very broad and groundless assumption on the part of the presence of the enzymes that metabolizes the drug. While MM equation has been one of the most widely known equations in biochemistry used in more than 220,000 published papers, the MM equation is accurate only when the concentration of the enzyme that metabolizes the drug is almost non-existent, causing the accuracy of the equation highly unsatisfactory – only 38 percent of the predictions had less than two-fold errors.
“To make up for the gap, researcher resorted to plugging in scientifically unjustified constants into the equation,” Professor Jung-woo Chae of Chungnam National University College of Pharmacy said. “This is comparable to having to have the epicyclic orbits introduced to explain the motion of the planets back in the days in order to explain the now-defunct Ptolemaic theory, because it was 'THE' theory back then.”
< (From left) Ph.D. student Yun Min Song (KAIST, co-first authors), Professor Sang Kyum Kim (Chungnam National University, co-corresponding author), Jae Kyoung Kim, CI (KAIST, co-corresponding author), Professor Jung-woo Chae (Chungnam National University, co-corresponding author), Ph.D. students Quyen Thi Tran and Ngoc-Anh Thi Vu (Chungnam National University, co-first authors) >
A joint research team composed of mathematicians from the Biomedical Mathematics Group within the Institute for Basic Science (IBS) and the Korea Advanced Institute of Science and Technology (KAIST) and pharmacological scientists from the Chungnam National University reported that they identified the major causes of the FDA-recommended equation’s inaccuracies and presented a solution.
When estimating the gut bioavailability (Fg), which is the key parameter of the equation, the fraction absorbed from the gut lumen (Fa) is usually assumed to be 1. However, many experiments have shown that Fa is less than 1, obviously since it can’t be expected that all of the orally taken drugs to be completely absorbed by the intestines. To solve this problem, the research team used an “estimated Fa” value based on factors such as the drug’s transit time, intestine radius, and permeability values and used it to re-calculate Fg.
Also, taking a different approach from the MM equation, the team used an alternative model they derived in a previous study back in 2020, which can more accurately predict the drug metabolism rate regardless of the enzyme concentration. Combining these changes, the modified equation with re-calculated Fg had a dramatically increased accuracy of the resulting estimate. The existing FDA formula predicted drug interactions within a 2-fold margin of error at the rate of 38%, whereas the accuracy rate of the revised formula reached 80%.
“Such drastic improvement in drug-drug interaction prediction accuracy is expected to make great contribution to increasing the success rate of new drug development and drug efficacy in clinical trials. As the results of this study were published in one of the top clinical pharmacology journal, it is expected that the FDA guidance will be revised according to the results of this study.” said Professor Sang Kyum Kim from Chungnam National University College of Pharmacy.
Furthermore, this study highlights the importance of collaborative research between research groups in vastly different disciplines, in a field that is as dynamic as drug interactions.
“Thanks to the collaborative research between mathematics and pharmacy, we were able to recify the formula that we have accepted to be the right answer for so long to finally grasp on the leads toward healthier life for mankind.,” said Professor Jae Kyung Kim. He continued, “I hope seeing a ‘K-formula’ entered into the US FDA guidance one day.”
The results of this study were published in the online edition of Clinical Pharmacology and Therapeutics (IF 7.051), an authoritative journal in the field of clinical pharmacology, on December 15, 2022 (Korean time).
Thesis Title: Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction (doi: 10.1002/cpt.2824)
< Figure 1. The formula proposed by the FDA guidance for predicting drug-drug interactions (top) and the formula newly derived by the researchers (bottom). AUCR (the ratio of substrate area under the plasma concentration-time curve) represents the rate of change in drug concentration due to drug interactions. The research team more than doubled the accuracy of drug interaction prediction compared to the existing formula. >
< Figure 2. Existing FDA formulas tend to underestimate the extent of drug-drug interactions (gray dots) than the actual measured values. On the other hand, the newly derived equation (red dot) has a prediction rate that is within the error range of 2 times (0.5 to 2 times) of the measured value, and is more than twice as high as the existing equation. The solid line in the figure represents the predicted value that matches the measured value. The dotted line represents the predicted value with an error of 0.5 to 2 times. >
For further information or to request media assistance, please contact Jae Kyoung Kim at Biomedical Mathematics Group, Institute for Basic Science (IBS) (jaekkim@ibs.re.kr) or William I. Suh at the IBS Communications Team (willisuh@ibs.re.kr).
- About the Institute for Basic Science (IBS)
IBS was founded in 2011 by the government of the Republic of Korea with the sole purpose of driving forward the development of basic science in South Korea. IBS has 4 research institutes and 33 research centers as of January 2023. There are eleven physics, three mathematics, five chemistry, nine life science, two earth science, and three interdisciplinary research centers.
2023.01.18 View 20870 -
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 23117