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Main Research Topics

1. Computational Biology (Molecular Modeling) The most distinctive feature of this research field is to use computer for studying biological phenomena. The state of art modern computational methods for bioscience study include molecular dynamics (MD) simulation, molecular docking, homology modeling etc. Here are the proteins we are currently working on:
  • SUMO/Ubiquitin
  • HslUV/CodXW (ATP-dependent Protease)
  • Thioredoxin (Trx)
  • Micro RNAs: PAZ
  • Protein Engineering: D-Pscicos Epimerase

2. Computer-Aided Molecular(Drug) Design: This hot field means the systematic molecular design using computer and it consists of 2D/3D QSAR (Quantitative Structure-Activity Relationships) and developing pharmacophore model and data searching for the new lead molecules. This is the protein list:

  • PPAR¥ã (Peroxisome Proliferator-Activated Receptor-¥ã)
  • MetRS(Methionyl-tRNA Synthetase)
  • PTP1B( Protein Tyrosine Phosphatase 1B)
  • LCK (Specific Protein Tyrosine Kinase)
  • AK (Adenosine Kinase)

3. Machine Learning(ML)/ML-CADD: Since Google demonstrated the potential of machine learning (ML) via AlphaGO to the world, the ML and bigdata now became TWO most important keywords of science as well as industria. We are also interested especially in combining of ML and CADD:

  • ML-based QSAR Generator
  • Deep Learning for ADME & Toxicity Prediction
  • Constrution of ML-based CADD Server



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1. ÄÄÇ»Å͸ðÀǽÇÇè/ºÐÀڸ𵨸µ(Computer Simulation/Molecular Modeling): °è»ê»ý¹°ÇÐÀÇ °¡Àå ´ëÇ¥ÀûÀÎ ¿¬±¸ºÐ¾ß·Î¼­ °í¼º´É ½´ÆÛÄÄÇ»Å͸¦ ÀÌ¿ëÇÑ ¸ðÀǽÇÇè°è»êÀ» ¼öÇàÇÏ¿© ´Ü¹éÁúÀ̳ª Çٻ갰Àº °Å´ëºÐÀÚµéÀÇ »óÈ£ÀÛ¿ëÀ̳ª ±¸Á¶º¯È­µî¿¡ ´ëÇÏ¿© ¿¬±¸ÇÑ´Ù. °¡Àå´ëÇ¥ÀûÀÎ ¸ðÀǽÇÇè¹æ¹ýÀº ´ºÆ°¹æÁ¤½ÄÀ» Ç®¾î¼­ ´Ü¹éÁú/Çٻ갰Àº °Å´ëºÐÀÚÀÇ ½Ç½Ã°£ ¿òÁ÷ÀÓÀ» °üÂûÇÏ´Â ºÐÀÚµ¿·ÂÇиðÀǽÇÇè (Molecular Dynamics Simulation)°ú °áÇÕ¹æ½ÄÀÌ ¾Ë·ÁÁöÁö ¾ÊÀº ´Ü¹éÁú°ú ÁöÁúÀÇ »óÈ£ÀÛ¿ë conformationÀ» ¾Ë¾Æ³»´Â ºÐÀÚµµÅ·¸ðÀǽÇÇè (Molecular Docking Simulation) ¹æ¹ýµîÀÌ ÀÖ´Ù. ¶ÇÇÑ ¾ÆÁ÷ 3Â÷¿ø ÀÔü±¸Á¶°¡ ¹àÇôÁöÁö ¾ÊÀº ´Ü¹éÁúÀÇ ÀÔü±¸Á¶¸¦ ½ÃÄö½ºÁ¤º¸·Î ºÎÅÍ ¾Ë¾Æ³»´Â È£¸ô·ÎÁö¸ðµ¨¸µ(Homology Modeling)µî ´Ù¾çÇÑ ¿¬±¸±â¹ýµîÀ» È°¿ëÇÏ¿© ´Ü¹éÁú-±âÁú ȤÀº ´Ü¹éÁú-´Ü¹éÁú°£ÀÇ »óÈ£ÀÛ¿ëÀ» ¿¬±¸ÇÑ´Ù. ´ÙÀ½Àº ÇöÀç ¿¬±¸ÁßÀÎ ´Ü¹éÁú ¸ñ·ÏÀÌ´Ù:
  • SUMO/Ubiquitin
  • HslUV/CodXW (ATP-dependent Protease)
  • Thioredoxin (Trx)
  • Micro RNAs: PAZ
  • Protein Engineering: D-Pscicos Epimerase

2. ÄÄÇ»Å͸¦ ÀÌ¿ëÇÑ ½Å¾à¼³°è(Computer-Aided Drug Design, CADD): ÀÌ ºÐ¾ß´Â ÇÕ¸®ÀûÀÌ°í ü°èÀûÀÎ ¹æ¹ý·ÐÀ» ÅëÇÏ¿© Ç×¾ÏÁ¦ ȤÀº Ç×»ýÁ¦°°Àº »õ·Î¿î ½Å¾à ȤÀº ½Å¾àÈĺ¸¹°ÁúÀ» µµÃâÇÏ´Â ¿¬±¸ºÐ¾ß·Î¼­ ¸ðµç ÁÖ¿ä Á¦¾àȸ»ç¿¡¼­ »ç¿ëÇÏ°í ÀÖ´Â ¸Å¿ì ºÎ°¡°¡Ä¡°¡ ³ôÀº Áß¿äÇÑ ¿¬±¸ºÐ¾ßÀÌ´Ù. °ø°Ý ¸ñÇ¥ ´Ü¹éÁú¿¡ ´ëÇÑ 2Â÷¿ø/3Â÷¿ø Á¤·®Àû±¸Á¶È°¼º°ü°è (2D/3D QSAR, Quantitative Structure-Activity Relationships)¸¦ ¿¬±¸ÇÏ°í ¶ÇÇÑ ÇÊ¿äÇÑ pharmacophore ¸ðµ¨À» µµÃâÇÏ°í À̸¦ Åä´ë·Î ÀûÀýÇÑ database screeningÀ» ½Ç½ÃÇÑ´Ù. ´ÙÀ½Àº ÇöÀç ¿¬±¸ÁßÀÎ ´Ü¹éÁú ȤÀº ½Ã½ºÅÛ ¸ñ·ÏÀÌ´Ù:

  • PPAR¥ã (Peroxisome Proliferator-Activated Receptor-¥ã)
  • MetRS(Methionyl-tRNA Synthetase)
  • PTP1B( Protein Tyrosine Phosphatase 1B)
  • LCK (Specific Protein Tyrosine Kinase)
  • AK (Adenosine Kinase)

3. ¸Ó½Å·¯´×(ML)/ML-CADD(Machine Learning (ML)-CADD): 2016³â GoogleÀÌ ¾ËÆÄ°í(AlphaGO) ¸¦ ·ŽÇØ ¸Ó½Å·¯´×ÀÇ °¡´É¼ºÀ» ¼¼°è¿¡ ¼±º¸ÀÌÀÚ, ÇöÀç ¸ðµç ¿¬±¸ºÐ¾ß´Â ¹°·ÐÀÌ°í ±â¾÷ü¿¡¼­µµ ºòµ¥ÀÌ¿Í ¸Ó½Å·¯´×À̶ó´Â µÎ Å°¿öµå¿¡ ¶ß°Å¿î °ü½ÉÀÌ ¸ð¾ÆÁö°í ÀÖ´Ù. ÀÌ·¯ÇÑ ¼¼°èÀûÀÎ Ãß¼¼¿¡ ¹ß ¸ÂÃß¾î º» ¿¬±¸½Ç¿¡¼­´Â ´ÙÀ½Ã³·³ ¸Ó½Å·¯´É ¿¬±¸±â¹ýµéÀ» ½Å¾à¼³°è ±â¹ýµé°ú ¿¬°áÇÏ´Â ºÐ¾ß ¿¬±¸¿¡ °ü½ÉÀ» ÁýÁßÇÏ°í ÀÖ´Ù: :

  • ML-based QSAR Generator
  • Deep Learning for ADME & Toxicity Prediction
  • Constrution of ML-based CADD Server





Research Target Proteins 1












Research Target Proteins 2



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