Skip Navigation
 

Jui-Hua Hsieh, Ph.D.

Molecular Toxicology and Informatics Group

Jui-Hua Hsieh
Jui-Hua Hsieh, Ph.D.
Visiting Fellow
Tel (919) 541-4482
jui-hua.hsieh@nih.gov
BG Keystone RM 2019 MSC K2-17
530 Davis DR
Durham, NC 27713

Jui-Hua Hsieh, Ph.D. is a Visiting Fellow in the Biomolecular Screening Branch of the Division of National Toxicology Program (NTP). She is working on developing human disease prediction models based on the quantitative high throughput screening (qHTS) data from Tox21 and chemical structures.

 

Prior to joining the NTP in 2011, she received her B.S. degree in Pharmacy from National Taiwan University and her Ph.D. degree in Pharmaceutical Sciences from University of North Carolina at Chapel Hill. Her Ph.D. work involves the development and application of cheminformatics tools for drug discovery projects, particularly hit identification in virtual screening. 


Selected Publications

National Toxicology Program

National Toxicology Program (NTP) Data and Reports Website 

  1. Hao Tang, Xiang S. Wang, Jui-Hua Hsieh, Alexander Tropsha. In press. Do crystal structures obviate the need for theoretical models of GPCRs for structure based virtual screening? Proteins
  2. Jui-Hua Hsieh, Shuangye Yin, Xiang S. Wang, Shubin Liu, Nikolay Dokholyan and Alexander Tropsha. Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge Based Pose Scoring and Physical Force Field-based Hit Scoring Functions Improves the Accuracy of Virtual Screening. J. Chem. Inf. Model.2011 [Epub ahead of print] [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/22017385) ]
  3. Jui-Hua Hsieh, Shuangye Yin, Alexander Sedykh, Nikolay Dokholyan and Alexander Tropsha
    Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets. Chem. Inf. Model. 2011, 51(9), 2027-35 [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/21780807) ]
  4. Jui-Hua Hsieh, Xiang S. Wang, Alexander Golbraikh, and Alexander Tropsha
    Differentiation of AmpC Beta-Lactamase Binders vs. Decoys using Classification kNN QSAR Modeling and Application of the QSAR Classifier to Virtual Screening. J. Comput. Aided Mol. Des. 2008 22(9) 593-609 [Abstract (http://www.ncbi.nlm.nih.gov/pubmed/18338225) ]

Back to Top