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BioLab
BioLab
충남대학교 신약전문대학원 화학정보설계실 Chemo-Informatics & Modeling Lab.
강남숙 교수
- 등록2025.05.26
- 조회711
연구실 소개
연구실 홈페이지Our research is focused on chemoinformatics and computer-aided drug design(CADD) for the drug discovery and development of molecules-of-interest. We design novel small molecules with various biological activities using computer, evaluate activity of designed molecules with co-work, and develop those molecules as potential candidates of drugs. Furthermore, we are developing a drug design method utilizing water networks to provide new insight for molecular design.
We also use artificial intelligence (A.I.) technology such as machine learning and deep learning to find hit compounds.
Nam Sook Kang, Ph.D. (강남숙)
Professor, Graduate School of New Drug Discovery and Development, Chungnam National University (CNU)
Education
1992 - 1995 Ph.D., Chemistry, KAIST
Professional Experience
2016~present: Director, BK21 Demand-directed Human Resource Project of Autonomous Convergence for Innovative Drug Discovery
2011~present: Professor, Graduate Scool of New Drug Discovery and Development, Chungnam National University (CNU)
2003~2011: Senior Researcher, Korea Research Institure of Chemical Technology (KRICT)
2002~2003: Visiting Research Fellows, Korea Institute of Science and Technology Information (KISTI)
1999~2002: Associate Research Engineer, Bioinformatics and Molecular Design Research Center (BMDRC)
1995~1999: Postdoctoral Researcher, Chemistry, KAIST
2011~present: Professor, Graduate Scool of New Drug Discovery and Development, Chungnam National University (CNU)
2003~2011: Senior Researcher, Korea Research Institure of Chemical Technology (KRICT)
2002~2003: Visiting Research Fellows, Korea Institute of Science and Technology Information (KISTI)
1999~2002: Associate Research Engineer, Bioinformatics and Molecular Design Research Center (BMDRC)
1995~1999: Postdoctoral Researcher, Chemistry, KAIST
연구분야
Chemoinformatics
Chemoinformatics refers to use of physicochemical properties of molecules-in-interest with computer and "in silico" techniques to find out drugable hit compounds of target disease. Such in silico techniques are used to aid and inform the process of drug discovery, in the design of well-defined combinatorial libraries of synthetic compounds, or to assist in structure based drug design.
Encoding chemical structures
Molecules cannot be fed into machine learning tools without encoding them. The chemical structure needs to be transformed into a numerical description of the molecule to develop mathematical models that relate chemical structures to biological activities. The mathematical disciplines of graph theory and geometry, among others, provide techniques to encode molecules. The resulting numerical representation is called molecular descriptor. Molecular descriptors can be used for a number of predictive modeling tasks such as virtual high-throughput screening, visualizing chemical libraries, the analysis of quantitative structure-activity relationships, and for predicting a molecule’s target structure.
Development and validation of chemoinformatic models
Most machine learning techniques can be used to develop chemoinformatic models. The employed molecular descriptor is of utmost importance for the successful predictive modeling. If the numerical description of the molecule is unsuitable for the purpose, good results are rather unlikely. Since molecular descriptors are mostly complex and high dimensional descriptions of chemical molecules, data analysis may be prone to chance correlation and overfitting. Rigorous validation and assessment of the resulting models is therefore essential to exclude seemingly good models that would perform badly in the productive phase of the model.
Molecular Modeling
Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies. Various strategies exist in Molecular Modeling, for example, structure-based drug design (SBDD) using the three-dimensional structure of proteins, ligand-based drug design (LBDD) using the pharmacophoric features of ligand, fragment-based drug design (FBDD) and Denovo Design. An appropriate strategy must be selected according to the target protein. We mainly develop inhibitors for protein kinase that mediates signaling by phosphorylation in living organisms.
When a ligand binds to a disease-related target protein, entropy increases as water molecules in the binding site go out. This is called the hydrophobic effect, and the protein-ligand is bound by the thermodynamic process of these water molecules. In the past, computer-based drug design (CADD) research methods considered only proteins or ligands. But recently, studies consider the physicochemical properties of these water molecules more actively.
However most known algorithms require calculation of the total free energy of the system and research is concentrated only on the inner surface of the protein binding site (first layer). Topological water network (TWN) considered that water molecules can be quantified without free energy calculation. TWN can consider not only the protein surface but also the inner (second layer). TWN consists of hydrogen bonds in ring-shaped polygons such as 3-ring, 4-ring, 5-ring, and 6-ring formed among water molecules.
However most known algorithms require calculation of the total free energy of the system and research is concentrated only on the inner surface of the protein binding site (first layer). Topological water network (TWN) considered that water molecules can be quantified without free energy calculation. TWN can consider not only the protein surface but also the inner (second layer). TWN consists of hydrogen bonds in ring-shaped polygons such as 3-ring, 4-ring, 5-ring, and 6-ring formed among water molecules.
Employing the concept of TWN, we have optimized compounds for drug repurposing and selectivity. We are also developing methods that use TWN for predicting binding site similarity (TWN-RENCOD) and performing fragment screening (TWN-FS). We aim to further develop this into a methodology applicable to compound optimization and screening.
TWN-RENCOD
TWN-FS method
연구성과
최근 4년, 2022~ 현재
2025
2024
2023
2022
연구실 구성원
PI : Nam Sook Kang
Research Professor : Anand Balupuri
Ph.D. Course: Jonghwa Lee, Eun Kyung Chae, EunJu Song, Dong-Hyun Son, Re Gin Jeoung, Jisoon Lee
M.S. Course : Mi Jung Han, Jeong Hyeon Kim, BoMi Jeong, Ji Hyeon Kim
Homepage : http://ccim.cnu.ac.kr/cim/index.do
Contact : nskang@cnu.ac.kr
하고싶은 이야기
화학정보설계실은 4단계 BK21 참여 연구실로서
1. 신약개발 산업체 인턴 연구 지원과 공동 연구를 통한 기업체 맞춤형 신약개발 전문 연구원 양성
2. 컴퓨터 기반 (인공지능 기술 포함) 화학정보/설계 기술을 이용한 난치병 치료제 연구
3. 컴퓨터 기반 분자수준의 시뮬레이션을 이용한 질환 메커니즘 및 신약개발 방법 연구
를 목적으로 하고있습니다.
화학 정보 설계 및 분자 설계, 신약 개발에 관심이 있는 석박통합과정 연구생이나 석사과정 연구생을 모집합니다.
-등록금, 생활비 지원
-BK21 참여대학원생 장학금지급
-성과에 따른 인센티브 지급
-국내/ 해외 학술대회 참가 경비지원
지원을 원하는 학생들은 nskang@cnu.ac.kr 로 이메일을 보내주세요.
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