Grids/CI for Scholarly Research and application to Chemical Informatics

1. Grids/CI for Scholarly Researchand application toChemical Informatics HPC 2006 in Cetraro – Italy July 4 2006 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 gcf@indiana.edu http://www.infomall.org

2. Motivation • Build Cyberinfrastructure (Grids) that • Support science from beginning (planning, instruments) through middle (analysis) and end (refereed publications, follow-on work) • Integrates with the popular Web 2.0 (community) tools whose successes point to interesting ways of working together • Integrate with Digital Library technology • Does not redo previous work but rather augments it • Assumes a heterogeneous fragmented world with multiple platforms • Allows one to specify and manage all the services and data that a project needs with a mix of synchronous, asynchronous, close (classic workflow) and loose (including zero) coupling

3. Application Drivers • Chemical Informatics as this has very precise naming rules for compounds that allow accurate searches in documents • Suggesting how to tag scientific documents either when writing it or after the fact • “Global Information Grid” (Military Net-Centric systems) as these inevitably need Grid of Grids to support “systems of systems” • Journal web site of the future as illustrated by Nature building social bookmarking tool Connotea • Conference support tools as can benefit from features needed by journals

4. The Science Drivers • From Workshop on Challenges of Scientific Workflows http://vtcpc.isi.edu/wiki/index.php/Main_Page • Workflow is underlying support for current science model • Distributed interdisciplinary data deluged scientific methodology as an end (instrument, conjecture) to end (paper, Nobel prize) process is a transformative approach • Reproducibility core to scientific method and requires rich provenance, interoperable persistent repositories with linkage of open data and publication as well as distributed simulations, data analysis and new algorithms. • Distributed Science Methodology publishes all steps in a new electronic logbook capturing scientific process (data analysis) as a rich cloud of resources including emails, PPT, Wikis as well as databases, compiler options, build time/runtime configuration…

5. Community (? VO) Tools • e-mail and list-serves are oldest and best used • Kazaa, Instant Messengers, Skype, Napster, BitTorrent for P2P Collaboration – text, audio-video conferencing, files • del.icio.us, Connotea, Citeulike, Bibsonomy, Biolicious manage shared bookmarks (later) • MySpace, Bebo, Hotornot, Facebook, or similar sites allow you to create (upload) community resources and share them; Friendster, LinkedIn create networks • http://en.wikipedia.org/wiki/List_of_social_networking_websites • Writely, Wikis and Blogs are powerful specialized shared document systems • ConferenceXP and WebEx share general applications • Google Scholar (Citeseer) tells you who has cited your papers while publisher sites tell you about co-authors • Windows Live Academic Search has similar goals (later) • Note sharing resources creates (implicit) communities • Social network tools study graphs to both define communities and extract their properties

6. How to use Web2.0 Community tools in CI • Nearly all of them have “profiles”, “users”, “groups”, “friends” etc. • Need to integrate these • P2P File Sharing: Maybe this is useful for sharing files in research groups (virtual organizations) • Will modify Maze http://maze.pku.edu.cn– popular Chinese social P2P system with 2.5 million users • BitTorrent: more popular than FTP – why not use for higher performance fault tolerant cached file sharing? • MySpace etc.: Could consider MyGridSpace or MyScienceSpace that supports a similar document sharing model with users uploading pictures, papers and even data/services of interest • Could include uploaded material in workflows • Can impose different policies • Social Bookmarking and linking: discuss later • http://gf6.ucs.indiana.edu:48990/SemanticResearchGrid/

7. SSG Domain-1 Web service Tool-1 Del.icio.us Tool-2 Connotea Tool-3 MySpace Tool–N e.g. CiteSeer SSG Domain-N Web service Native UI-1 Native UI-4 Native UI-3 Native UI-N SSG MDStore Gateway WS-1 Gateway WS-2 Gateway WS-3 Gateway WS-N Integrated User Interface UI SSG = Semantic Scholars’ Grid Integration Framework of Tools

8. Strategy • Doesn’t seem useful to build the 251st community tool • In fact a major barrier to use of existing tools is • What happens when a better tool comes along and/or chosen tool disappears (unsupported/removed from Web) • So assume use existing tools but wrap them all as web services so can transfer information to new tools and integrate information between tools • Need some “glue” logic, a “unification” database and minimal user interface • Bookmarking tools: del.icio.us, Connotea, CiteULike (includes plug-ins to major publisher sites) • Document: Google Scholar, Windows Live, Citeseer tools, OSCAR3 for Chemistry (later), Science.gov • Journals: Manuscript Central • Conferences: CMT from Microsoft or ?

9. Connotea

10. Connotea queried by SERVOGrid

11. Delicious Semantic Web/Grid • http://del.icio.us purchased by Yahoo for ~$30M • http://www.CiteULike.org • http://www.connotea.org (Nature) • Associate metadata with Bookmarks specified by URL’s, DOI’s (Digital Object Identifiers) • Users add comments and keywords (called tags) • Users are linked together into groups (communities) • Information such as title and authors extracted automatically from some sites (PubMed, ACM, IEEE, Wiley etc.) • Bibtex like additional information in CiteULike • This is perhaps de facto Semantic Web – remarkable for its simplicity

12. Document-enhanced Cyberinfrastructureaka Semantic Scholar Grid I • Citeseer and Google Scholar scour the Internet and analyze documents for incidental metadata • Title, author and institution of documents • Citations with their own metadata allowing one to match to other documents • Science.gov extracts metadata from lots of US Government databases • These capabilities are sure to become more powerful and to be extended • Give “Citation Index” in real time • Tell you all authors of all papers that cite a paper that cites you etc. (Note it’s a small world so don’t go too far in link analysis) • Tell you all citations of all papers in a workshop

13. Document-enhanced Cyberinfrastructureaka Semantic Scholar Grid II • It is natural to develop core document Servicessuch as those used in Citeseer/Google Scholar but applied to “your” documents of interest that may not have been processed yet • As just submitted to a conference perhaps • These tools can help form useful lists such as authors of all cited or submitted papers to a journal • OSCAR2/3 (from Peter Murray-Rust’s group at Cambridge) augment the application independent “core” metadata (Title, authors, institutions, Citations) with a list of all chemical terms • This tool is a Service that can be applied to “your” document or to a set of documents harvested in some fashion • Other fields have natural application specific metadata and OSCAR like tools can be developed for them • Such high value tools could appear on “publisher” sites of future (or else publishers will disappear)

14. MyResearchDatabase Bibliographic Database Web serviceWrappers Document-enhanced Cyberinfrastructure Del.icio.us Windows Live Academic Search TraditionalCyberinfrastructure Export:RSS, BibtexEndnote etc. CiteULike Google Scholar Connotea Citeseer Bibsonomy Science.gov Biolicious PubChem Generic Document Tools CMT ConferenceManagement PubMed Manuscript Central Community Tools Integration/Enhancement User Interface etc. Existing User Interface New Document-enhanced Research Tools Existing Documentbased Research Tools

15. Chemical Informatics as a Grid Application • Chemical Informatics is the application of information technology to problems in chemistry. • Example problems: managing data in large scale drug discovery and molecular modeling • Building Blocks: Chemical Informatics Resources: • Chemical databases maintained by various groups • NIH PubChem, NIH DTP, http://nihroadmap.nih.gov/ • Application codes (both commercial and open source) • Data mining such as clustering • Quantum chemistry and molecular modeling • Screening centers (with HTS High Throughput Screening devices) measuring interaction of chemicals with biological samples • Visualization tools • Web resources: journal articles, etc. • Chemical Informatics Gridhttp://www.chembiogrid.org needs to integrate these into a common, loosely coupled, distributed computing environment.

16. Oracle Database (HTS) Word Document (Marketing) Journal Article Word Document (Chemistry) Computation External Database (Patent) Excel Spreadsheet (Toxicity) Computation Oracle Database (Genomics)  The information in the structures and known activity data is good enough to create a QSAR model with a confidence of 75%  A report by a team in Marketing casts doubt on whether the market for this target is big enough to make development cost-effective Compounds were tested against related assays and showed activity, including selectivity within target families  A recent journal article reported the effectiveness of some compounds in a related series against a target in the same family  All the compounds pass the Lipinksi Rule of Five and toxicity filters ? None of these compounds have been tested in a microarray assay  One of the compounds was previously tested for toxicology and was found to have no liver toxicity  Some structures with a similarity > 0.75 to these appear to be covered by a patent held by a competitor  Several of the compounds had been followed up in a previous project, and solubility problems prevented further development Document, Simulation and Data rich CI for Chemical Informatics ? SCIENTIST “These compounds look promising from their HTS results. Should I commit some chemistry resources to following them up?”

17. HTS results and COMPARE Web service Positive results (red bar to right of vertical line) indicates greater than average toxicity of cell line to tested agent. http://dtp.nci.nih.gov/docs/compare/compare.html

18. HTS data organization & flagging A tumor cell line is selected. The activity results for all the compounds in the DTP database in the given range are extracted from the PostgreSQL database OpenEye FILTER is used to calculate biological and chemical properties of the compounds that are related to their potential effectiveness as drugs VOPlot Taverna The compounds are clustered on chemical structure similarity, to group similar compounds together The compounds along with property and cluster information are converted to VOTABLES format and displayed in VOPLOT Use Taverna for Workflow and VOTable (from astronomy) as basic data structure; VOTable of compounds and properties with Excel-like spreadsheet services

19. Varuna environment for molecular modeling (Baik, IU) Chemical Concepts Researcher Papers etc. Experiments ChemBioGrid Simulation ServiceFORTRAN Code, Scripts DB ServiceQueries, Clustering,Curation, etc. ReactionDB QM Database Condor PubChem, PDB,NCI, etc. QM/MM Database Supercomputer

20. OSCAR3 Service from Cambridge UK • Oscar3 is a tool for shallow, chemistry-specific natural language parsing of chemical documents (i.e. journal articles). • It identifies (or attempts to identify): • Chemical names: singular nouns, plurals, verbs etc., also formulae and acronyms. • Chemical data: Spectra, melting/boiling point, yield etc. in experimental sections. • Other entities: Things like N(5)-C(3) and so on. • Uses SMILES, InChI and CML • There is a larger effort, SciBorg, in this area • http://www.cl.cam.ac.uk/~aac10/escience/sciborg.html http://wwmm.ch.cam.ac.uk/wikis/wwmm/index.php/Oscar3

21. OSCAR2 Chemistry Document analysis • It detects “magic” chemical strings in text and then • Stores them as metadata associated with document • Queries ChemInformatics repositories to tell you lots of information about identified compounds • Tells you which other documents have this compound

22. Clustering Documents from chemicalproperties

23. Provenance and Delicious CI • We can use del.icio.us style interface to annotate Application Data with (extra) provenance and user comments of any type (describing quality of data or a keyword relating different data etc.) • All data should be labeled by a URI to enable this • One has in addition Citeseer/OSCAR metadata • Current major tagging systems support flat list of tags without name=value (RDF triple) or schema organization • RDF Triples << Full Semantic Web • Delicious << RDF • Tradeoff between features and pervasive deployment • Some extra features are easy to add as a custom service • Features not supported by del.icio.us can be uploaded as comments

24. Current Status • Google Scholar, Windows Live Academic Search, del.icio.us, Connotea, CiteULike, OSCAR3 are Web Services • Debugging on 500 presentations and papers from my CGL research group • Experiment with GGF Presentations, Broad collection of Chemical Informatics resources (explore science document CI link) and Concurrency&Computation: Practice&Experience Web site (?business model for journals)

25. Collection (Grid) Builder Tool • This can perhaps be built on top of workflow systems • Unlike ordinary workflow, this is a tool to manage collections of Grids and the key metadata adorning Grids and Services • It instantiates needed mediation between Grids (systems) to convert • JMS to MQSeries • GT4 to WS-I+ • WS-Eventing to WS-Notification • It supports conventional workflow as tightly coupled services • It supports system wide “management” (configuration) • We are using WS-Management – see CLADE paper • Deploy services and mediation brokers on demand to deliver real-time performance • DoD can’t pause the battle while WS-RM and TCP catch up if data saturated

26. CPUs Clusters Compute Resource Grids Overlay and Compose Grids of Grids MPPs Methods Services Component Grids Federated Databases Databases Data Resource Grids Sensor Sensor Nets Grids of Grids of Simple Services • Grids are managed collections of one or more services • A simple service is the smallest Grid • Services and Grids are linked by messages • Internally to service, functionalities are linked by methods • Link serices via methods  messages  streams • We are familiar with method-linked hierarchyLines of Code  Methods  Objects  Programs  Packages

27. Component Grids? • So we build collections of Web Services which we package as component Grids • Visualization Grid • Sensor Grid • Utility Computing Grid • Collaboration Grid • Earthquake Simulation Grid • Control Room Grid • Crisis Management Grid • Drug Discovery Grid • Bioinformatics Sequence Analysis Grid • Intelligence Data-mining Grid • We build bigger Grids by composing component Grids

28. Port Port Port InternalInterfaces InternalInterfaces InternalInterfaces Port Port Port Port Port Port Port Port Port Grid or Service Grid or Service Grid or Service Mediation and Transformation in a Grid of Grids and Simple Services Mediation and Transformation Services Distributed Brokers between distributed ports Mediation and Transformation Services Listen, Queue Transform, Send External facing Interfaces Mediation and Transformation Services 1-10 ms Overhead Use “OGSA” to Federate?

29. 1 Chip 8 Core/chip 2 Chips 1 Core/chip 1 Chip 6 Core/chip Opteron 2 Chips 2 Core/chip Xeon 4 Cores is 3000 messages per second; about one message per millisecond per core for Opteron; one message per 2 ms for Sun Niagara core

30. Pentium 4 (3.4GHz) with 1GB of RAM while IBM- MQ Series, Naradabrokering and the Message Bridge are all running on it.NaradaBrokering running in JMS emulation mode

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