COMPUTATIONAL CHEMOGENOMICS FOR DRUG ABUSE (CC4DA)
PI: Xiang-Qun (Sean) Xie, PhD, MBA
The Computational Chemogenomics Core for Drug Abuse (CC4DA, or Core A) of CDAR, is proposed specifically to address existing fundamental challenges of drug abuse (DA) and medication research to enhance the NIH/NIDA funded research projects (FRP), with particular attention to polydrug addiction and polypharmacology, via systematic investigation of the interaction between chemical compounds and DA-related protein targets, as well as the underlying signaling pathways. We will build upon our existing chemogenomics and computational expertise in the realm of cannabinoid research and expand our developed computational algorithms and tools to facilitate research in the general domain of DA. To accomplish the proposed objectives, the CC4DA Core will: (i) enable data sharing and processing among scientists in the DA and related scientific communities by our established public chemogenomics knowledgebase for drug abuse (DA-KB); (ii) integrate and further advance our established computational algorithms and tools for exploration and prediction of DA targets, DA pathways, the underlying mechanisms, and the potential polypharmacological effects that relate to polydrug addiction; and (iii) implement cloud computing and sourcing DA research services (CloudDA) to facilitate in silico design, discovery and development of new medications with potential for DA treatment. Strategies used to complete the proposed work will overcome knowledge barriers of advanced computer modeling, complicated machine learning methods and high-performance computing for scientists/users in the fields of pharmaceutical, neurobiological and pharmacological sciences. Successful development of DA-KB and computational algorithms for DA research (DAR) will provide powerful tools for analyzing a wide variety of chemicals, and effectively assisting scientists in the rational design of DA therapeutics.
Computational Biology: http://www.compbio.pitt.edu/Faculty/index.html