CPRI Computational Core
The Computational Core commonly engages in ligand (‘hit’) identification via virtual screening and/or de novo drug design and hit optimization/prioritization through the use of ligand-target modeling and computational ADMET predictors. Such computational strategies, in conjunction with evaluative validation studies (see Translational Core), help guide and/or prioritize medicinal chemistry (see Synthesis Core) and can dramatically expedite drug discovery/development and/or translational research objectives. This core operates under the directorship of Professor Chang-Guo Zhan with a current staff of one Ph.D. level computational chemist.
As a protein therapeutics complement to this core, the Molecular Modeling and Biopharmaceutical Center (MMBC) within the College of Pharmacy supports innovative research development and applications of computational modeling-based approaches to understanding and exploiting protein structure, function and dynamics as well as capabilities and expertise in biopharmaceutical discovery and development.
Core Services and Publications
Ligand Based Screening
- Study design/consultation
- Virtual screen of ≥100K compounds using existing validated ligands as the query
Structure Based Screening
- Study design/consultation
- Virtual screen of ≥100K compounds using an existing protein/enzyme target structure as the query
De novo Drug Design
- Study design/consultation
- Design 3-5 diverse ligand scaffolds for an existing protein/enzyme target structure
Chemoinformatics and ADMET Analysis
- Study design/consultation
- Predictive toxicology analysis (BIOVIA Discovery Studio TOPKAT®)
- Chemoinformatics (CDD Vault®, BIOVIA Discovery Studio and SimulationsPlus ADMET Predictor®)
- PK/bioavailability analysis (SimulationsPlus ADMET Predictor® and GastroPlus®, Certara Phoenix® WinNonlin® and ICON NONMEM®)
Representative Publications
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Li Z, Huang Y, Wu Y, Chen J, Wu D, Zhan CG, Luo HB (2019). Absolute binding free energy calculation and design of a subnanomolar inhibitor of phosphodiesterase-10. Journal of Medicinal Chemistry 62(4):2099-2111. PMCID: N/A
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Yuan Y, Zheng F, Zhan CG (2018). Improved prediction of blood-brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints. AAPS Journal 20(3):54. PMCID: In Process
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Jin Y, Huang X, Papke RL, Jutkiewicz EM, Showalter HD, Zhan CG (2017). Design, synthesis, and biological activity of 5'-phenyl-1,2,5,6-tetrahydro-3,3'-bipyridine analogues as potential antagonists of nicotinic acetylcholine receptors. Bioorganic & Medicinal Chemistry Letters 27(18):4350-4353. PMCID: PMC5592152
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Sviripa VM, Burikhanov R, Obiero JM, Yuan Y, Nickell JR, Dwoskin LP, Zhan CG, Liu C, Tsodikov OV, Rangnekar VM, Watt DS (2016). Par-4 secretion: Stoichiometry of 3-arylquinoline binding to vimentin. Organic & Biomolecular Chemistry 14(1):74-84. PMCID: PMC4681656
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Miller Z, Kim KS, Lee DM, Kasam V, Baek SE, Lee KH, Zhang YY, Ao L, Carmony K, Lee NR, Zhou S, Zhao Q, Jang Y, Jeong HY, Zhan CG, Lee W, Kim DE, Kim KB (2015). Proteasome inhibitors with pyrazole scaffolds from structure-based virtual screening. Journal of Medicinal Chemistry 58(4):2036–2041.
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Burikhanov R, Sviripa VM, Hebbar N, Zhang W, Layton WJ, Hamza A, Zhan CG, Watt DS, Liu C Rangnekar VM (2014). Arylquins target vimentin to trigger Par-4 secretion for tumor cell apoptosis. Nature Chemical Biology 10(11):924-926. PMCID: PMC4201913
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Zhang W, Sviripa V, Chen X, Shi J, Yu T, Hamza A, Ward NS, Kril LM, Vander Kooi CW, Zhan CG, Evers BM, Watt DS, Liu C (2013). Fluorinated N,N-dialkylaminostilbenes repress colon cancer by targeting methionine S-adenosyltransferase 2A. ACS Chemical Biology 8(4):796-803. PMCID: PMC3631441
Inquiries
The Computational Core welcomes inquiries from UK investigators with an interest in the application of chemoinformatics, predictive toxicology/biodistribution, computational modeling, or rational design of novel ligands for on-going or emerging research projects. For more information and to request services, please contact us.
Funding Acknowledgment Statement
This work was supported by the Center for Pharmaceutical Research and Innovation (CPRI, NIH P20 GM130456) and the National Center for Advancing Translational Sciences (UL1 TR001998).