Chemoinformatics

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Chemoinformatics

• David Wild, djwild_at_indiana.edu
• Bioinformatics Retreat, Feb 2nd, 2007

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Current state of chemoinformatics research

• What works and what doesnt
• Fingerprints, clustering and diversity
• QSAR - predictive and descriptive methods,
  virtual screening
• 3D similarity, pharmacophores docking
• Visualization, organization and navigation of
  chemical datesets
• Current buzz areas in chemoinformatics
• How can we use our internal strengths to do
  something new, important and impressive?

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What works and what doesnt

• 2D structure and similarity searching well
  established
• Lots of papers comparing fingerprints for
  similarity
• Some recent evidence Scitegic ECFPs better for
  recall of actives
• Clustering well established but definite room for
  improvement
• Traditional methods Wards, K-means, Jarvis
  Patrick
• Recently single pass similarity cutoff methods
  used for very fast organization - gt0.85 for
  similar activity, gt0.55 for QSAR
• Data mining methods - ROCK, Chameleon, Cure, etc
  unexplored
• Diversity hot -gt cold -gt smart
• QSAR - poor relation of academic work to industry
  usefulness
• Lots of papers this method works best on this
  dataset
• Random forests appear practically to work rather
  well
• Interpretability vs predictive ability
• Predictive methods for LogP, pKa, solubility, etc
  work reasonably
• Virtual screening virtually useless unless tied
  in with HTS screening process. However, is useful
  for exploring around leads.

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What works and what doesnt

• Mostly, 3D methods havent worked out yet
• Similarity QSAR - Almost every paper 2D better
  for recall and precision but 3D methods give
  interesting ideas. Useful for lead hopping
• Pharmacophore searching not widely used
• Docking - very useful for visual inspection, poor
  correlation of scoring functions with binding
• Visualization, organization and navigation of
  datasets
• Still not clear how to work with datasets gt few
  hundred compounds
• Dot plots, spreadsheet-based methods work
  minimally
• Need for UI design and research

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The current buzz in chemoinformatics

• Decorporatization and commoditization of data and
  software
• MLSCN, PubChem, open source, small companies
• Crisis for the software companies, nice for
  academia
• Pharma companies in the brown stuff without a
  paddle
• Integration with other ics
• Data mining chemical/genomic information
• Linking compounds -gt proteins -gt pathways, etc
  (e.g. KEGG)
• Fuzzy boundaries, integration with science and
  informatics
• Microsoft 2020 vision for science
• Integration of text and structure searching
• Semantic web, services and mashups will probably
  have a BIG impact exporting best of breed what
  happens to the rest?

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Suggested collaboration areas

• Chem/bio/complex systems mashups using web
  services in each of the areas nice, confined
  projects for students once you have the
  infrastructure
• Chem and complex can work together on integrating
  text and structure-based searching, indexing and
  crawling (e.g. networks of web services and
  databases), and intelligent agents
• Data mining of chemogenomic information
• Integration of advanced chemoinformatics methods
  with systems biology and pathway mapping tools
• Performing research to establish best practices
  for areas of chemoinformatics
• Tackling algorithmic problems for which there is
  currently no good solution - docking and scoring

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Cyberinfrastructure

• Geoffrey Fox
• Computer Science, Informatics and Physics

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Cyberinfrastructure

• Supports distributed science data, people,
  computers
• Exploits Internet technology (Web2.0) adding (via
  Grid technology) management, security,
  supercomputers etc.
• It has two aspects parallel low latency
  (microseconds) between nodes and distributed
  highish latency (milliseconds) between nodes
• Parallel needed to get high performance on
  individual 3D simulations, data analysis etc.
  must decompose problem
• Distributed aspect integrates already distinct
  components
• Cyberinfrastructure is in general a distributed
  collection of parallel systems
• Cyberinfrastructure is made of services (usually
  Web services) that are just programs or data
  sources packaged for distributed access

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TeraGrid Integrating NSF Cyberinfrastructure
TeraGrid is a facility that integrates
computational, information, and analysis
resources at the San Diego Supercomputer Center,
the Texas Advanced Computing Center, the
University of Chicago / Argonne National
Laboratory, the National Center for
Supercomputing Applications, Purdue University,
Indiana University, Oak Ridge National
Laboratory, the Pittsburgh Supercomputing Center,
and the National Center for Atmospheric
Research. Today 100 Teraflop tomorrow a
petaflop Indiana 20 teraflop today.
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Cyberinfrastructure at IU

• Interpreted broadly (Web presences), there are
  many activities at IU
• Interpreted narrowly as the programmable web or
  using Grid technologies there are large
  projects in atmospheric, earthquake, ice-sheet
  sciences, network systems, particle physics,
  Crystallography and Cheminformatics
• IU has an international reputation in both
  parallel and distributed Cyberinfrastructure
  including education, research and resources
• IU has 31 Supercomputer in world and is part of
  two major National activities TeraGrid and Open
  Science Grid
• There are several well known Bioinformatics Grids
  such as BIRN (mainly images) and caBIG (cancer
  databases) from NIH and MyGrid from UK (EBI)
• Could be opportunities to link Biology and
  Informatics/CS in Cyberinfrastructure projects

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Cyberinfrastructure motivated by Web 2.0

• Capture the power of interactive Web/Grid sites
  enabling people to create, collaborate and build
  on each others work

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Web services, workflows, portals and ontologies

• Web Services allow us to quickly develop and
  deploy new tools, interfaces that cross
  disciplines and are broadly accessible
• Can use simple HTTP and ignore Web Service
  complications
• Workflows (called mashups in Web 2.0) allow us to
  string together collections of web services to do
  computation that is tailored to the science (as a
  one-off or for re-use).
• Develop core capabilities as services and use in
  many different ways as in 770 Google map mashups
• APIs/Languages/Data structures/Ontologies (WSDL
  AJAX JSON at low level) allow us to describe
  workflows and services in discoverable, standard
  ways, such that reasoning tools can piece them
  together to match queries
• Portals enable composable reusable user
  interfaces
• Distributed posting of services and easily
  available composition tools enable everybody to
  contribute
• Interesting implications for broader
  participation

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Model and Data Sharing

• Cyberinfrastructure requires agreed sharing
  standards (data structures, APIs, protocols,
  ontologies, languages) as intrinsically
  internationally distributed
• There are agreed data structures for taking
  Sequence?Protein?Folding?Interaction
  Transparently, e.g. BLAST
• Nothing at the level where genomics and
  proteomics is important cells and tissues.
• Partial answers CellML, FieldML, SBML which do
  not link to relevant standards outside Biology
• Need to connect models at these levels. Need
  Standard ontologies/data structures for cell
  behaviors to allow connections and validation
• Need to connect Models like SBW (Systems Biology
  Workbench)/BioSpice -gtCell-level models
  (Compucell) -gtTissue level models (Physiome)
• Model builders at these scales not
  CS-sophisticated. Models NOT interoperable and
  dont use useful general ideas
• Glazier organizing activity in this area with H.
  Sauro (U. Washington), W. Li (UCSD-SDSC), Hunter
  (U. Auckland) and NIH
• Link to Open Grid Forum standard setting and
  community activities

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http//www.chembiogrid.org

• Database enabled quantum chemistry computations
• Services to link PubChem, Supercomputers, results
  of high throughput Screening centers
• Education IU has unique Cheminformatics degrees
• Portals

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Chemical Informatics web service infrastructure

• Database Services
• Local NIH DTP Human Tumor Cell Line set
• Local PubChem mirror
• Derived properties database
• Pub3D, PubDock
• Synonym service
• VARUNA quantum chemistry database
• Statistics (based on R)
• Regression, Neural Nets, Random Forest
• LDA
• K-means clustering
• Plotting
• T-test and distribution sampling

• Computation Services
• OpenEye FRED, OMEGA, FILTER,
• Cambridge OSCAR3
• BCI fingerprint generation, Wards, Divisive
  K-means clustering
• Tox Tree
• Similarity fingerprint calculations (CDK)
• Descriptor calculation (CDK)
• 2D structure diagrams (CDK)
• 2D-gt3D File format conversions

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Workflows - Taverna (taverna.sourceforge.net)
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(No Transcript)
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PubDock - Chimera-based interface
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Kemo - A ChatBot for PubChem

• Uses ALICE chatbot www.alicebot.org
• AIML used to define knowledge base, e.g. reaction
  to common phrases like FIND ME, WHAT IS THE LOGP
  OF, etc
• Can iteratively improve knowledge base
• Accesses PubChem through web service interface

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Workflow in Xbaya - a meteorology tool!
http//www.extreme.indiana.edu/xgws/xbaya/
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Indexing the worlds chemical informationAND
computational functionality

• Crawl and index web pages, journal articles, etc.
  for
• Structures (InChIs, SMILES)
• Images (converted using Clide or ChemReader)
• Names (converted using OSCAR3 or similar package)
• Other information (IR spectra, reactions, etc)
• Technology still immature, but improving quickly
• Problem with access to journal articles we will
  assume open access in the future!
• Expose computational functionality as web
  services, contextualize in an OWL-S ontology
  (semantics), and publish in a UDDI
• Now we know what information we have, and what we
  can do with it
• Develop bots and intelligent agents to
  automatically do useful things