- Dr. Yuanqiang Wang received certification for outstanding research award from University of Pittsburgh School of Pharmacy
- CPDA Summer Annual Meeting, Boston, July 16-18, 2018
Introduction of NIDA CDAR Center
The National Center of Excellence for Computational Drug Abuse Research (CDAR), is a joint initiative between the University of Pittsburgh (Pitt) and Carnegie Mellon University (CMU), funded by the NIH (NIDA).
The goals of the NIDA CDAR will be:
- to advance and implement state-of-the-art computational chemical genomics (chemogenomics) technologies for facilitating drug abuse (DA) prevention and treatment research.
- to centralize the newly developed drug abuse chemogenomics knowledgebase (DA-KB) through a cloud computing/sourcing server platform in order to enable efficient information exchange among DA researchers and related scientific communities, and to accelerate the development of novel intervention methods for preventing and treating DA and addiction.
- to enhance DA research (DAR) by synergistically leveraging the activities of ongoing funded research projects (FRPs) and by mentoring/training junior researchers in the field.
To achieve these goals, we have structured the NIDA CDAR Center in three Research Support Cores (or Cores) that will operate under the leadership of three PIs with complementary expertise:
- Core A: The Computational Chemogenomics Core for DA (CC4DA) led by Dr. Sean Xie (Pitt)
- Core B: The Computational Biology Core for DA (CB4DA) led by Dr. Ivet Bahar (Pitt)
- Core C: The Computational Genomics Core for DA (CG4DA) led by Dr. Eric Xing (CMU)
The overarching aim of the Cores is to develop, implement and enable broad usage of computational technology (algorithms, methods, software and tools) for enhancing the effectiveness of DA research, both on a local level (by supporting ongoing funded projects at Pitt/CMU) and at the national level (via close cooperation with other labs, including the NIDA Centers Consortium members).
Overall, the Center will pursue the long-term goal of translating the advances in computational chemistry, biology, and genomics to facilitate developing efficient personalized DA therapeutics and neuropharmacology research.