Prof. Lee-Wei Yang lab use a spectrum of physics models to study protein/RNA conformational changes and the consequent protein-drug and protein-protein interaction; The dynamics features are used to understand biomolecular functions, predict enzyme active sites, protein-DNA binding sites and protein-protein binding interfaces etc. Normal mode analysis, time-dependent linear response theory and molecular dynamics simulations are used to refine X-ray/NMR-determined structure complexes, understand intra-/intermolecular signal communication, design antimicrobial peptides/ anticancer drugs, explore protease degradation mechanism, study down-regulation of autophagic flux to suppress tumors in xenografted mice, design peptide drugs/ antimicrobial peptides and understand ribosomal helicase activities in unwinding secondary structures of mRNA. He also develop new bioinformatics tools such as fast comparison tools for long text strings and chromosome identification using algorithms implemented for Next Generation Sequencing.

  The techniques he used in research include but are not limited to Machine Learning Algorithms, Protein Dynamics, Polymer Physics, Molecular mechanics, Coarse-Grained modeling and simulations, Enzymology, Active site prediction, Physical model-based prediction/analysis of NMR relaxation data and temperature factors of X-ray determined structures, Structure Refinement, DNA Polymerase, Homology modeling, Biological and dynamics database/online calculation tools development, Statistical analysis of biomolecular traits in dynamics, Computational techniques for large matrix decomposition, Linear response theory, Normal Mode Analysis, Langevin Dynamics, Theoretical reconciliation of lock-and-key and induce-fit paths on substrate binding.

https://cls.site.nthu.edu.tw/p/406-1469-191865,r7382.php?Lang=en

The research focuses of Dr. Lin have been on the development of novel methods for computational drug discovery and structural bioinformatics, and their innovative applications for exploring new therapeutic candidates and biomolecular targets of natural product molecules and other pharmaceutical agents. 

  As a recent example of such efforts, Dr. Lin’s lab published a computational scheme for binding free energy calculations based on first principle of statistical mechanics, that can be applied to general biomolecular interactions, including proteins, peptides, and small chemical molecules. Besides, Dr. Lin’s lab has committed in improving the accuracy of prediction of molecular docking methods, and their applicable domains. Examples of novel applications in this area include, MEDock, SLITHER and idTarget, and the last one was the first docking-based proteome-wide target prediction for a given small synthetic chemical molecule or a natural product compound. Deep learning algorithms and other artificial intelligence approaches have also been incorporated in the development of new computational methods for computational drug design. 

  He is the inventor of 16 patents for the new drugs discovered from his collaborative teams. Two series of drug candidates on neurodegenerative diseases and cancer treatment were exclusively licensed to the companies in Taiwan in 2015 and 2016, respectively. Starting in the February of 2025, one small molecule drug for treating Alzheimer’s disease is in the Phase I First in Human clinical trial.

https://www.rcas.sinica.edu.tw/web/mem_faculty/Lin_JungHsin_en.html

   Professor Hsien-Tai Chiu of the Department of Chemistry at National Cheng Kung University leads the Taiwan BT&D² Team, an interdisciplinary research group integrating chemistry, biochemistry, bioinformatics, and AI-driven drug discovery. His team develops an advanced AI-powered BT&D² Medical and Pharmaceutical R&D System that combines computational prediction, chemical–biological synthesis, enzyme-based modification, and Traditional Chinese Medicine (TCM) extraction technologies. The platform accelerates the discovery and optimization of small-molecule drugs and TCM formulations, offering capabilities such as drug–indication prediction, AI-guided structural modification, formulation improvement, and bio-efficacy testing. Through international collaboration and cross-disciplinary innovation, Professor Chiu’s laboratory aims to significantly reduce the cost and time of developing new therapeutic products.

Prof. Yi-Cheng Chang focused on East Asian-specific genetic variants of type 2 diabetes, obesity, fatty liver, and kidney diseases. He use transgenic mice to dissect the molecular mechanism, develop small-molecular drugs, and conduct clinical trials.

East Asians are relatively genetically isolated. He participated in large-scale East Asian whole-genome scanning cooperation projects, such as TOPMed and SAPPHIRe. He discovered several novel East Asian-specific genetic variants for diabetes and obesity, such as the acetaldehyde dehydrogenase (ALDH2) mutation (E487K) and Canton-type G6PD genetic mutations.

He found that the ALDH2 East Asian-specific knockin mice developed obesity, abnormal glucose homeostasis, fatty liver, impaired renal function, and cardiomyopathy. He collaborated with Stanford University and pharmaceutical companies to develop potent ALDH2 activators which can scavenge harmful aldehydes and rescue these pathological conditions in mice. Among them, the preventive effect of acute kidney injury of ALDH2 activators will enter the second phase of clinical trials. He has also tested the therapeutic effects of small-molecule G6PD activators that can reverse obesity, abnormal glucose homeostasis, cardiomyopathy, and nephropathy caused by the Canton-type G6PD mutation in mice.

In addition, he also identified that RRBP1 deficiency in mice caused hyporeninemic hypoaldosteronism, resulting in lower blood pressure, severe hyperkalemia, and sudden cardiac death. In juxtaglomerular cells, deficiency of RRBP1 reduced renin intracellular trafficking from ER to Golgi apparatus. RRBP1 is a brand-new regulator of blood pressure and potassium homeostasis discovered in this study.

https://www.ibms.sinica.edu.tw/pi_webpage/en/index7.html