We propose to establish a NIDA Center of Excellence for Computational Drug Abuse Research (CDAR) between the University of Pittsburgh (Pitt) and (CMU), with the goal of advancing and ensuring the productive and broad usage of state-of-the-art computational technologies that will facilitate and enhance drug abuse (DA) research, both in the local (Pittsburgh) area and nationwide. To this end, we will develop/integrate tools for DA-domain-specific chemical-to-protein-to-genomics mapping using cheminformatics, computational biology and computational genomics methods by centralizing computational chemical genomics (or chemogenomics) resources while also making them available on a cloud server. The Center will foster collaboration and advance knowledge-based translational research and increase the effectiveness of ongoing funded research project (FRPs) via the following Research Support Cores: (1) The Computational Chemogenomics Core for DA (CC4DA) will help address polydrug addiction/polypharmacology by developing new chemogenomics tools and by compiling the data collected/generated, along with those from other Cores, into a DA knowledge-based chemogenomics (DA-KB) repository that will be made accessible to the DA community. (2) The Computational Biology Core (CB4DA) will focus on developing a resource for structure-based investigation of the interactions among substances of DA and their target proteins, in addition to assessing the drugability of receptors and transporters involved in DA and addiction. These activities will be complemented by quantitative systems pharmacology methods to enable a systems-level approach to DA research. (3) The Computational Genomics Core (CG4DA) will carry out genome-wide discovery of new DA targets, markers, and epigenetic influences using developed machine learning models and algorithms. (4) The Administrative Core will coordinate Center activities, provide management to oversee the CDAR activities in consultation with the Scientific Steering Committee (SSC) and an External Advisory Board (EAB), ensure the effective dissemination of software/data among the Cores and the FRPs, and establish mentoring mechanisms to train junior researchers. Overall, the Center will strive to achieve the long-term goal of translating advances in computational chemistry, biology and genomics toward the development of novel personalized DA therapeutics.

Public Health Relevance

We propose a Computational Drug Abuse Research (CDAR) Center, as a joint initiative between the University of Pittsburgh and Carnegie Mellon University. The Center consist of three Cores (CC4DA, CB4DA and CG4DA) that will leverage our expertise in computational chemogenomics, computational biology, and computational genomics to facilitate basic and translational drug abuse and medication research.

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Center Core Grants (P30)
Project #
Application #
Study Section
Special Emphasis Panel (ZDA1)
Program Officer
Hillery, Paul
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Schools of Pharmacy
United States
Zip Code
Kaya, Cihan; Cheng, Mary H; Block, Ethan R et al. (2018) Heterogeneities in Axonal Structure and Transporter Distribution Lower Dopamine Reuptake Efficiency. eNeuro 5:
Xue, Ying; Feng, Zhi-Wei; Li, Xiao-Ye et al. (2018) The efficacy and safety of cilostazol as an alternative to aspirin in Chinese patients with aspirin intolerance after coronary stent implantation: a combined clinical study and computational system pharmacology analysis. Acta Pharmacol Sin 39:205-212
Jing, Yankang; Bian, Yuemin; Hu, Ziheng et al. (2018) Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS J 20:58
Wang, Haohan; Liu, Xiang; Xiao, Yunpeng et al. (2018) Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model. Methods 145:33-40
Hu, Ziheng; Wang, Lirong; Ma, Shifan et al. (2018) Synergism of antihypertensives and cholinesterase inhibitors in Alzheimer's disease. Alzheimers Dement (N Y) 4:542-555
Ponzoni, Luca; Zhang, She; Cheng, Mary Hongying et al. (2018) Shared dynamics of LeuT superfamily members and allosteric differentiation by structural irregularities and multimerization. Philos Trans R Soc Lond B Biol Sci 373:
Krieger, James; Lee, Ji Young; Greger, Ingo H et al. (2018) Activation and desensitization of ionotropic glutamate receptors by selectively triggering pre-existing motions. Neurosci Lett :
Bian, Yuemin; Xie, Xiang-Qun Sean (2018) Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications. AAPS J 20:59
Yoo, Young Dong; Mun, Su Ran; Ji, Chang Hoon et al. (2018) N-terminal arginylation generates a bimodal degron that modulates autophagic proteolysis. Proc Natl Acad Sci U S A 115:E2716-E2724
Zhou, Lili; Zhou, Shan; Yang, Peng et al. (2018) Targeted inhibition of the type 2 cannabinoid receptor is a novel approach to reduce renal fibrosis. Kidney Int 94:756-772

Showing the most recent 10 out of 78 publications