CCGS Center has its research on target identification and system pharmacology for drug lead/chemical probe discovery and cell signaling mechanism studies, with over 100 publications and invention discovery patents. The innovative work was achieved by our developed GPU-accelerated cloud computing TargetHunter machine-learning programs and diseases-specific chemogenomics knowledgebase.
The platform of “Big data to knowledge” (www.CBLigand.org/CCGS) was built on our extensive knowledge and solid working experience in development of know-how technologies and application of in silico design and virtual screening, computational chembiology, medicinal chemistry optimization, and biophysics/biochemistry validation for pharmacometrics system pharmacology and translational drug discovery research.
Our recent work on Alzheimer's disease specific chemogenomics database was on 2014 coverpage story of a top peer-reviewed ACS journal. The innovation work includes GPU-accelerated cloud computing TargetHunter program for drug target identification (www.CBLigand.org/CCGS/TargetHunter) (2013 AAPS special theme issue).
The overall goal of the proposed Computational Chemical Genomics Screening Center (CCGSC) is to build a research/teaching platform and collaboration services by providing new exploratory computational tools/algorithms and chemical libraries resources in a chemical genomics scale for in-silico drug design and discovery. This goal is related to, but distinct from, cheminformatics, computational biology, bioinformatics, medicinal chemistry and pharmacology.
The objective is the more rapid identification of novel drug-like molecules, so called “lead compounds,” and their associated biological targets embracing multiple early phase drug discovery processes ranging from computational target identification and validation, to in silico screening, lead modification/scaffold hopping and virtual compound library design, and to in silico ADME profiling.
CCGSC will have a mission to promote interdisciplinary research, education and training, and foster collaborations to develop state-of-the-art computational-chemical-genomics-based in-silico drug design approaches through exploration of chemical-diversity space and its relationship to biological space.
Director: Xiang-Qun (Sean) Xie, PhD,MBA
Xiang-Qun (Sean) Xie, MD, PhD, EMBA (中文简历)
Associate Dean for Research Innovation and Professor of Pharmaceutical Sciences, School of Pharmacy
Director of Computational Chemical Genomics Screening (CCGS) Center
Director of NIH National Center of Excellence for Computational Drug Abuse Research (CDAR).
Dr Xiang-Qun (Sean) Xie is an Associate Dean for Research Innovation at School of Pharmacy and a Professor of Pharmaceutical Sciences/Drug Discovery Institute, and a PI of an integrated Medicinal Chemistry Biology laboratory of CompuGroup, BioGroup and ChemGroup at University of Pittsburgh. He is a member of the Science Advisory Board to the US FDA. He is a Founding Director of Computational Chemical Genomics Screening Center (www.CBLigand.org/CCGS), and a Director/PI of NIH National Center of Excellence for CDAR (www.CDARCenter.org). He holds joint positions at Dept of Computational Biology and Dept of Structural Biology, and Pitt Cancer Institute MT/DD Program.
Dr. Xie is an Editorial Advisory Board member for AAPS Journal and American Journal of Molecular Biology, and Associate Editor of BMC Pharmacology Toxicology. He was a Charter Member of NIH BPNS Study Section, and an ad hoc expert reviewer for UK MRC foundation; the Wellcome Trust Fund; the Netherland Organization for Scientific Research Council; the Austrian Science Fund (FWF) Erwin Schrödinger Fellowship; and the Chinese Natural Science Foundation.
Dr. Xie also holds/held honorary professorship in top institutes and colleges of pharmacy in China, including Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Stem Cell Medical Center; and Shanghai Jiaotong University. He was an invited International Assessment Panelist for Fudan University, a member of the Board of Directors of the Chinese Association of Professionals in Science and Technology, and a Chair of the CAPST-Biomedical & Pharmaceutical Society.
Prof Xie and his team are known for their pioneering research for the development of internationally recognized diseases domain-specific chemogenomics knowledgebases, which is an integrated platform of "Big Data to Knowledge" computational chemogenomics-based target identification and system pharmacology for translational research. His recent work on Alzheimer's disease specific chemogenomics database (www.CBLigand.org/CCGS/AD/) was on 2014 coverpage story of a top peer-reviewed ACS journal. The innovation work includes GPU-accelerated cloud computing TargetHunter program for drug target identification (www.CBLigand.org/CCGS/TargetHunter) (2013 AAPS special theme issue). His lab was the first discovered/patented INK4C-targeting small molecule inhibitors for hematopoietic stem cell expansion (Nature Comm 2015), was the first discovered/patented p62ZZ chemical inhibitors for multiple myeloma (Nature Leukemia 2015), and also reported/patented novel ligands specific to cannabinoid CB2 receptor for osteoporosis and cancers. His invention discovery patents have been successfully licensed out to Biotech/Pharma. Overall, his developed integrated cloud computing knowledgebases help to bridge the knowledge gap between biology and chemistry, and to facilitate target identification, drug repurposing, and system pharmacology analyses in a chemogenomics scale for precision medicine drug discovery. As a result, he is a recipient of 2014 AAPS Award for Outstanding Research Achievements.
Currently, there are limited computing resources (hardware and software) to directly support computational chemical genomics and drug design research. Some resources are already available from the University Centers/Institutes. In addition, some computing facilities already exist in the participating faculty labs or in their home Departments.
Currently CCGS is equipped with:
9 Linux PCs
2 SGI workstations
Dell Web-server/File-server workstation
30-CPU Dell dual-cores/dual nodes Linux-based cluster
Tripos software packages, Amber, CYANA, AutoDock and other computer modeling packages
Full access to the computing facilities at PSC and DCB
Upon establishment of the CCGSC, its members will be closely connected through the gigabit backbone campus network to the faculty in the DPS, DCB, UPDDI, UPCMLD, PSC, and faculty labs where other hardware and software resources and staff support services already exist.
CCGS Center has its research on target identification and system pharmacology for drug lead/chemical probe discovery and cell signaling mechanism studies with over 100 publications and invention discovery patents.
p62/SQSTM1/Sequestosome-1 is an N-recognin of the N-end rule pathway which modulates autophagosome biogenesis
Xie2-64, a novel CB2 receptor inverse agonist, reduces cocaine abuse-related behaviors in rodents
Small-molecule inhibitors targeting INK4 protein p18INK4C enhance ex vivo expansion of hematopoietic stem cells
TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database
Blocking the ZZ Domain of Sequestosome 1/p62 Suppress the Enhancement of Myeloma Cell Growth and Osteoclast Formation by Marrow Stromal Cells
AI Deep Learning Molecular Generative Modeling of Scaffold-Focused Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries
Analysis of Substance Use and Its Outcomes by Machine Learning I. Childhood Evaluation of Liability to Substance Use Disorder.
Analysis of Substance Use and Its Outcomes by Machine Learning: II. Derivation and Prediction of the Trajectory of Substance Use Severity
Off-target prediction by searching the available bioactive compound-target pairs reported
from literature using the query structure
Chemogenomics database for Alzheimer's disease (AD) is designed and constructed to collect multiple AD related protein targets and their ligands to explore the potential pharmacology of a small molecule for anti-AD
Web-interfaced cannabinoid molecular information database is designed to facility data-sharing and information exchange for Cannabinoid research with online structure search functions and data analysis tools implemented
Chemogenomics Database for Drug abuse Research is designed for facilitating data-sharing and research communities for drug abuse, including genes, proteins, small molecules and signal pathways, with online structure search functions and data analysis tools implemented
Chemogenomics database for Cardiovascular disease (CVD) is designed and constructed to collect multiple CVD related protein targets and their ligands to explore the potential pharmacology of a small molecule for anti-CVD
Chemogenomics database for hallucinogen research is designed and constructed to collect multiple hallucinogen- related protein targets and their ligands to explore the mechanisms of hallucinogens
Chemogenomics Knowleage Base for Stem Cell (SC) Research is designed to collect multiple SC-related protein targets and their ligands to explore the potential pharmacology of a small molecule for stem cell research
BBB permeability prediction by AdaBoost and SVM combining with 4 different fingerprints were employed to predict if a compound can pass the BBB(BBB+) or can not pass the BBB(BBB-)