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Aiding Computer Aided Drug Design

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slides presented in USM Ipharm

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Aiding Computer Aided Drug Design

  1. 1. Aiding Computer Aided Drug Design Mohd Shahir Shamsir (PhD) Bioinformatics Research Group (BIRG) Department of Biological Sciences, Faculty of Bioscience & Bioengineering Universiti Teknologi Malaysia
  2. 2. Summary <ul><li>Aiding Computer Aided Drug Design </li></ul><ul><li>Greasing the gears of CADD </li></ul><ul><li>Gaming gears </li></ul><ul><li>Video </li></ul>
  3. 3. CADD is a specialised discipline that uses computational methodsto simulate drug-receptor interactions – Richard M casey Computer aided drug design (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions Richard M. Casey
  4. 4. CADD methods are heavily dependent on bioinformatics tools, applications and databases. CADD methods are heavily dependent on bioinformatics tools, applications and databases.
  5. 5. CADD can metaphorically be an engine with many bioinformatics “gears” contributing towards the functioning of this “Engine”. Among these gears are homology modeling, similarity searchers, physicochemical modeling, virtual High Throughput screening, drug lead optimization, drug bioavailability, drug bioactivity, sequence analysis. All of these is expected to translate into reduced time-to-market, cost savings and new insight into drug research. Virtual High-Throughput Screening (vHTS) Sequence Analysis Homology Modeling Similarity Searches Drug Lead Optimization Physicochemical Modeling Drug Bioavailability and Bioactivity Cost Savings Time-to-Market Insight Computer Aided Drug Design
  6. 6. Docking gears for example have varieties of tools that have evolved
  7. 7. We can aid/assist CADD by ‘greasing’ the ‘gears’ . Greasing CADD
  8. 8. Interactions are key in ‘greasing’ the research gears. Science happens not just because of people doing experiments but because they are discussing those experiments Christopher Surridge, Managing Editor PLoS ONE Science happens not just because of people doing experiments but because they are discussing those experiments
  9. 9. Greasing using Web 2.0 would enable easier collaboration between researchers and research groups. Greasing using Web 2.0
  10. 10. Multiple tools mostly user generated that has user generated content in mind
  11. 11. Propels by a state of mind where users i.e. YOU generate content to be shared with other users
  12. 12. Community and social collaboration
  13. 13. Many examples of tools of web 2.0
  14. 14. Tools created by the African Continent – unrestricted creativity
  15. 15. Landscape breakdown of how these tool are connected to generate WEB 2.0
  16. 16. Easier to Collaborate <ul><li>Collaborative tools examples </li></ul><ul><li>Google Docs </li></ul><ul><li>Microsoft Office Live </li></ul><ul><li>Zoho </li></ul><ul><li>Thinkfree </li></ul><ul><li>Zimbra </li></ul><ul><li>springnote </li></ul>
  17. 17. Yugma – a web conferencing tool - http://www.yugma.com/
  18. 18. Freemind – a web based collaborative mind mapping tool - http://freemind.sourceforge.net/wiki/index.php/Main_Page
  19. 19. Slideshare – to share slides for reseach presentations - http://www.slideshare.net/
  20. 20. Research computing is currently very fragmented Existing approaches do not scale up to the amount of data now common Many chemical informatics tools are obscure, difficult to use and access Scientists’ questions are not that complex, but finding the answers is currently very time consuming and/or complex (for a human) “has anybody patented this chemical structure I just made?” “can I get hold of a compound that might bind to the active site of this protein I just resolved?” “which compounds in this series are least likely to exhibit toxic effects?” Answers are often “stale” after a short period of time – questions need to be re-answered as new information is generated Almost all available systems are passive, and follow the (web) browsing model There tends to be one interface for every data source (or encompassing just a few) Scalability passive Scalability issue Similarity Searches Research fragmentation Complex questions New pespective from new information usability Single interface accessibility Integrating CADD gears
  21. 21. Automated Bioinformatics Workflows Tools <ul><li>Examples such as </li></ul><ul><li>Taverna - http://taverna.sourceforge.net/ </li></ul><ul><li>Swami - http://www.ngbw.org/ </li></ul><ul><li>Systems Biology Workbench - http://www.sys-bio.org/index.htm </li></ul><ul><li>SCSC Workbench - http://workbench.sdsc.edu/ </li></ul>Integrating CADD gears
  22. 22. Amazon web services for easier biological data retrieval Easier data retrieval
  23. 23. <ul><li>Annotated Human Genome Data provided by  ENSEMBL (172GB). </li></ul><ul><li>GenBank provided by  the National Center for Biotechnology Information ( 250GB) </li></ul><ul><li>UniGene provided by  the National Center for Biotechnology Information  (10 GB) </li></ul>Easier data retrieval
  24. 24. Digital Collaborations
  25. 25. Initiated by O-Reilly meeting in 2004 called sci-foo
  26. 26. Replicated in Second life (SL)
  27. 27. Virtual meeting
  28. 28. Virtual meeting
  29. 29. Examples of material found on SL
  30. 30. Gaming gears for CADD Gaming Gears for CADD
  31. 31. PS3?
  32. 32. PS3!!
  33. 33. First PS3 cluster for research by Dr Frank Mueller of North Carolina University
  34. 34. Warhawk Server Cluster Costs less Reduced heat Minimum space
  35. 35. CUDA support by n-VIDIA
  36. 36. Statistics by Folding@Home
  37. 37. Up and running eHITS application using PS3 by SimBioSys Inc
  38. 38. Video about Web 2.0 VIDEO
  39. 39. CADD still need hard working people. CADD
  40. 40. credits Images from
  41. 41. Questions

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