%6e%65%77%20%64%72%75%67
-
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 2380 -
Bioengineers develop a new strategy for accurate prediction of cellular metabolic fluxes
A team of pioneering South Korean scientists has developed a new strategy for accurately predicting cellular metabolic fluxes under various genotypic and environmental conditions. This groundbreaking research is published in the journal Proceedings of the National Academy of Sciences of the USA (PNAS) on August 2, 2010.
To understand cellular metabolism and predict its metabolic capability at systems-level, systems biological analysis by modeling and simulation of metabolic network plays an important role. The team from the Korea Advanced Institute of Science and Technology (KAIST), led by Distinguished Professor Sang Yup Lee, focused their research on the development of a new strategy for more accurate prediction of cellular metabolism.
“For strain improvement, biologists have made every effort to understand the global picture of biological systems and investigate the changes of all metabolic fluxes of the system under changing genotypic and environmental conditions,” said Lee. The accumulation of omics data, including genome, transcriptome, proteome, metabolome, and fluxome, provides an opportunity to understand the cellular physiology and metabolic characteristics at systems-level. With the availability of the fully annotated genome sequence, the genome-scale in silico (means “performed on computer or via computer simulation.”) metabolic models for a number of organisms have been successfully developed to improve our understanding on these biological systems. With these advances, the development of new simulation methods to analyze and integrate systematically large amounts of biological data and predict cellular metabolic capability for systems biological analysis is important.
Information used to reconstruct the genome-scale in silico cell is not yet complete, which can make the simulation results different from the physiological performances of the real cell. Thus, additional information and procedures, such as providing additional constraints (constraint: a term to exclude incorrect metabolic fluxes by restricting the solution space of in silico cell) to the model, are often incorporated to improve the accuracy of the in silico cell.
By employing information generated from the genome sequence and annotation, the KAIST team developed a new set of constraints, called Grouping Reaction (GR) constraints, to accurately predict metabolic fluxes. Based on the genomic information, functionally related reactions were organized into different groups. These groups were considered for the generation of GR constraints, as condition- and objective function- independent constraints. Since the method developed in this study does not require complex information but only the genome sequence and annotation, this strategy can be applied to any organism with a completely annotated genome sequence.
“As we become increasingly concerned with environmental problems and the limits of fossil resources, bio-based production of chemicals from renewable biomass has been receiving great attention. Systems biological analysis by modeling and simulation of biological systems, to understand cellular metabolism and identify the targets for the strain improvement, has provided a new paradigm for developing successful bioprocesses,” concluded Lee. This new strategy for predicting cellular metabolism is expected to contribute to more accurate determination of cellular metabolic characteristics, and consequently to the development of metabolic engineering strategies for the efficient production of important industrial products and identification of new drug targets in pathogens.”
2010.08.05 View 21741 -
New drug targeting method for microbial pathogens developed using in silico cell
A ripple effect is expected on the new antibacterial discovery using “in silico” cells
Featured as a journal cover paper of Molecular BioSystems
A research team of Distinguished Professor Sang Yup Lee at KAIST recently constructed an in silico cell of a microbial pathogen that is resistant to antibiotics and developed a new drug targeting method that could effectively disrupt the pathogen"s growth using the in silico cell.
Hyun Uk Kim, a graduate research assistant at the Department of Chemical and Biomolecular Engineering, KAIST, conducted this study as a part of his thesis research, and the study was featured as a journal cover paper in the February issue of Molecular BioSystems this year, published by The Royal Society of Chemistry based in Europe.
It was relatively easy to treat infectious microbes using antibiotics in the past. However, the overdose of antibiotics has caused pathogens to increase their resistance to various antibiotics, and it has become more difficult to cure infectious diseases these days.
A representative microbial pathogen is Acinetobacter baumannaii. Originally isolated from soils and water, this microorganism did not have resistance to antibiotics, and hence it was easy to eradicate them if infected. However, within a decade, this miroorganism has transformed into a dreadful super-bacterium resistant to antibiotics and caused many casualties among the U.S. and French soldiers who were injured from the recent Iraqi war and infected with Acinetobacter baumannaii.
Professor Lee’s group constructed an in silico cell of this A. baumannii by computationally collecting, integrating, and analyzing the biological information of the bacterium, scattered over various databases and literatures, in order to study this organism"s genomic features and system-wide metabolic characteristics. Furthermore, they employed this in silico cell for integrative approaches, including several network analysis and analysis of essential reactions and metabolites, to predict drug targets that effectively disrupt the pathogen"s growth. Final drug targets are the ones that selectively kill pathogens without harming human body.
Here, essential reactions refer to enzymatic reactions required for normal metabolic functioning in organisms, while essential metabolites indicate chemical compounds required in the metabolism for proper functioning, and their removal brings about the effect of simultaneously disrupting their associated enzymes that interact with them.
This study attempted to predict highly reliable drug targets by systematically scanning biological components, including metabolic genes, enzymatic reactions, that constitute an in silico cell in a short period of time.
This research achievement is highly regarded as it, for the first time, systematically scanned essential metabolites for the effective drug targets using the concept of systems biology, and paved the way for a new antibacterial discovery. This study is also expected to contribute to elucidating the infectious mechanism caused by pathogens.
"Although tons of genomic information is poured in at this moment, application research that efficiently converts this preliminary information into actually useful information is still lagged behind. In this regard, this study is meaningful in that medically useful information is generated from the genomic information of Acinetobacter baumannii," says Professor Lee. "In particular, development of this organism"s in silico cell allows generation of new knowledge regarding essential genes and enzymatic reactions under specific conditions," he added.
This study was supported by the Korean Systems Biology Project of the Ministry of Education, Science and Technology, and the patent for the development of in silico cells of microbial pathogens and drug targeting methods has been filed.
[Picture 1 Cells in silico]
[Picture 2 A process of generating drug targets without harming human body while effectively disrupting the growth of a pathogen, after predicting metabolites from in silico cells]
2010.04.05 View 25461