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University of Maryland Baltimore
Experimental Therapeutics Symposium 2009

Published in: Education, Technology
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  1. 1. CADD Center 2009 update Identify Target: Biological and Structural Data Virtual Database Screening (Docking) from 1M to 100 cmpds 10 4 enhancement Biological Assay Lead Compounds Lead Optimization Drug Candidate Compound selection for assay Dissimilarity and physico-chemical properties Pharmacokinetics Toxicology etc.
  2. 2. Virtual commercially available scompound database 1 million compounds for 3D docking studies 5.4 million compounds for similarity searching Updated annually
  3. 3. CADD GCC-related Activities
  4. 4. Methodological developments for lead compound optimization CADD based lead optimization requires accurate mathematical modeling of a large number of chemical entities: Challenge for computational chemistry! CHARMM general force field ( CgenFF ): Force field for pharmaceutical compounds compatible with the highly optimized CHARMM biomolecular force fields.
  5. 5. Methodological developments for Lead compound optimization <ul><li>SILCS: Site Identification by Ligand Competitive Saturation </li></ul><ul><li>Computational approach to fragment based drug design </li></ul><ul><li>“ Rigorous” free energy evaluation of relative ligand affinity </li></ul><ul><ul><li>Protein flexibility Explicit solvent representation </li></ul></ul><ul><li>Allow multiple ligands to compete for sites on the entire protein surface in explicit solvent MD simulations </li></ul>
  6. 6. SILCS
  7. 7. Tier 1 SILCS H-bond donor, H-bond acceptor, Aliphatic and Aromatic Fragmaps Red: H-bond acceptor, Blue: H-bond donor, Green: aliphatic Purple: aromatic SMRT (A) and BCOR (B) peptides as CPK representation
  8. 8. Acknowledgements Pedro Lopes, Olgun Guvench, Shijun Zhong, Xiao Zhu, Kenno Vanommeslaeghe, Chris Baker, Olgun Guvench, Xiao Zhu, Liz Hatcher, JiHyun Shim, Eva Darien, Prabhu Raman All collaborators NIH, NSF, DoD, Waxman Foundation, CADD Center (UM, SoP)