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Weifan Zheng, Ph.D.

Weifan Zheng, Ph.D.
Weifan Zheng, Ph.D., Associate Professor
  • Completed B.S. and M.S. in Chemistry in China; Ph.D. in Medicinal Chemistry, Cheminformatics & Molecular Modeling at The University of North Carolina at Chapel Hill
  • Worked for seven years in industry – three and half years as a Senior Research Scientist for Eli Lilly and Company and three and half years as an Investigator for GlaxoSmithKline
  • Received a Global Impact Award for cheminformatics at Eli Lilly and Company and a Millennium Award for cheminformatics at GlaxoSmithKline
  • Recognized with International Chemical Structure Association (CSA) Award for achievement in cheminformatics/QSAR for library design
  • Contact: wzheng@nccu.edu or 919-530-6752

Research Interests

Novel Computational Tools

This research seeks to improve the efficiency and effectiveness of large-scale virtual screening using both small molecule based and protein structure based approaches. Our goal is to create and validate new software tools. To date, three new methods have been developed.

Cheminformatics Approach to Model Neurodegenerative Disease Targets

This study seeks to establish structural models for critically important targets relevant to finding drugs for neurodegenerative diseases such as HDACs, PDEs and JNK.

Structure-Based Anesthetic Agent Discovery

Project is focused on establishing structure-based models for a recently identified target that is a potential surrogate for GABAa binding.


  • Ebalunode, JO, Zheng W, Tropsha A.  2011.  Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design. Methods Mol Biol. 685:111-33. Abstract
  • Ebalunode, JO, Jagun C, Zheng W.  2011.  Informatics approach to the rational design of siRNA libraries. Methods Mol Biol. 672:341-58. Abstract
  • Ebalunode, JO, Zheng W.  2010.  Molecular shape technologies in drug discovery: methods and applications. Curr Top Med Chem. 10:669-79. Abstract
  • Dong, X, Ebalunode JO, Cho SJ, Zheng W.  2010.  A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors. J Chem Inf Model. 50:240-50. Abstract
  • Adekoya, A, Dong X, Ebalunode J, Zheng W.  2009.  Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method. Curr Chem Genomics. 3:54-61. Abstract

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