Overview of Chemical Informatics and Cyberinfrastructure Collaboratory

1. Overview of Chemical Informatics and Cyberinfrastructure Collaboratory October 18 2006 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 gcf@indiana.edu http://www.infomall.org http://www.chembiogrid.org

2. Activities • Local Teams, successful Prototypes and International Collaboration set up in 3 initial major focus areas • Chemical Informatics Cyberinfrastructure/Grids with services, workflows and demonstration uses building on success in other applications (LEAD) and showing distributed integration of academic and commercial tools • Computational Chemistry Cyberinfrastructure/Grids with simulation, databases and TeraGrid use • Education with courses and degrees • Review of activities suggest we also formalize work in two further areas • Chemical Informatics Research – model applicability and data-mining • Interfacing with the User - interaction tools and portal optimized for particular customer groups • Also have started an activity to identify “customers” for Cyberinfrastructure and its implied Chemistry eScience model

3. CICC Senior Personnel • Peter T. Cherbas • Mehmet M. Dalkilic • Charles H. Davis • A. Keith Dunker • Kelsey M. Forsythe • Kevin E. Gilbert • John C. Huffman • Malika Mahoui • Daniel J. Mindiola • Santiago D. Schnell • William Scott • Craig A. Stewart • David R. Williams • Geoffrey C. Fox • Mu-Hyun (Mookie) Baik • Dennis B. Gannon • Marlon Pierce • Beth A. Plale • Gary D. Wiggins • David J. Wild • Yuqing (Melanie) Wu From Biology, Chemistry, Computer Science, Informatics at IU Bloomington and IUPUI (Indianapolis)

4. CICC Infrastructure Vision • Drug Discovery and other academic chemistry and pharmacologyresearch will be aided by powerful modern information technology ChemBioGrid set up as distributed cyberinfrastructure in eScience model • ChemBioGrid will provide portals (user interfaces) to distributed databases, results of high throughput screening instruments, results of computational chemical simulations and other analyses • ChemBioGrid will provide services to manipulate this data and combine in workflows; it will have convenient ways to submit and manage multiple jobs • ChemBioGrid will include access to PubChem, PubMed, PubMed Central, the Internet and its derivatives like Microsoft Academic Live and Google Scholar • The services include open-source software like CDK, commercial code from vendors from BCI, OpenEye, Gaussian and Google, and any user contributed programs • ChemBioGrid will define open interfaces to use for a particular type of service allowing plug and play choice between different implementations

5. Chemical Informatics and Cyberinfrastucture Collaboratory Funded by the National Institutes of Health www.chembiogrid.org CICC CICC CICC Combines Grid Computing with Chemical Informatics Large Scale Computing Challenges Science and Cyberinfrastructure CICC is an NIH funded project to support chemical informatics needs of High Throughput Cancer Screening Centers. The NIH is creating a data deluge of publicly available data on potential new drugs. Chemical Informatics is non-traditional area of high performance computing, but many new, challenging problems may be investigated. NIH PubMed DataBase OSCAR Text Analysis Cluster Grouping Toxicity Filtering Docking . Initial 3D Structure Calculation OSCAR-mined molecular signatures can be clustered, filtered for toxicity, and docked onto larger proteins. These are classic “pleasingly parallel” tasks. Top-ranking docked molecules can be further examined for drug potential. Chemical informatics text analysis programs can process 100,000’s of abstracts of online journal articles to extract chemical signatures of potential drugs. Molecular Mechanics Calculations Big Red (and the TeraGrid) will also enable us to perform time consuming, multi-stepped Quantum Chemistry calculations on all of PubMed. Results go back to public databases that are freely accessible by the scientific community. • CICC supports the NIH mission by combining state of the art chemical informatics techniques with • World class high performance computing • National-scale computing resources (TeraGrid) • Internet-standard web services • International activities for service orchestration • Open distributed computing infrastructure for scientists world wide NIH PubChem DataBase Quantum Mechanics Calculations IU’s Varuna DataBase POVRay Parallel Rendering Indiana University Department of Chemistry, School of Informatics, and Pervasive Technology Laboratories

6. CICC Prototype Web Services Basic cheminformatics Key Ideas Molecular weights Molecular formulae Tanimoto similarity 2D Structure diagrams Molecular descriptors 3D structures InChI generation/search CMLRSS R and Excel • Add value to PubChem with additional distributed services and databases • Develop nifty ideas like VOTables • Wrapping existing code in web services is not difficult • Provide “core” (CDK) services and exemplars of typical tools • Provide access to key databases via a web service interface • Provide access to major Compute Grids Next steps? Application based services • Define WSDL interfaces to enable global production of compatible Web services; refine CML • Add more services (identify gaps) • Add more databases, including 3D structural info • Demonstrate use of services in other pipelining tools (KDE, Knime – Pipeline Pilot already done) • Extend Computational Chemistry (Varuna) Services • Routine TeraGrid and Big Red use • “Production” on OSCAR3 CDK Gamess Jaguar • Develop more training material Compare (NIH) Toxicity predictions (ToxTree) Literature extraction (OSCAR3) Clustering (BCI Toolkit) Docking, filtering, ... (OpenEye)Varuna simulation

7. Web Service Locations Cambridge University • InChI generation / search • CMLRSS • OpenBabel Indiana University • Clustering • VOTables • OSCAR3 • Toxicity classification • Database services SDSCTypical TeraGrid Site InfoChem • SPRESI database NIH PubChem ….. Compare ….. Penn State University (now moved to IU) CDK based services • Fingerprints • Similarity calculations • 2D structure diagrams • Molecular descriptors

8. Cheminformatics Education at IU • Linked to bioinformatics in Indiana University’s School of Informatics • School of Informatics degree programs BS, MS, PhD • Programs offered at both the Indianapolis (IUPUI) and Bloomington (IUB) campuses • Bioinformatics MS and track on PhD • Chemical InformaticsMS and track on PhD • Informatics Undergraduates can choose a chemistry cognate (change to Life Sciences ) • PhD in Informatics started in August 2005 and offers tracks in • bioinformatics; chemical informatics; health informatics; human-computer interaction design; social and organizational informatics; more to come! • Good employer interest but modest student understanding of value of Cheminformatics degree • 3 core courses in Cheminformatics plus seminar/independent studies • Significant interest in distance education version of introductory Cheminformatics course (enrollment promising in Distance Graduate Certificate in Chemical Informatics)

9. Current Status • Web site http://www.chembiogrid.org • Wiki chosen to support project as a shared editable web space • Building Collaboratory involving PubChem – Global Information System accessible anywhere and at any time – enhance PubChem with distributed tools (clustering, simulation, annotation etc.) and data • Adopted Taverna as workflow as popular in Bioinformatics but we will evaluate other systems such as GPEL from LEAD • Demonstrated CI-enhanced Chemistry simulations • Initiated Data-mining,User interface and Chemical Informatics tools research • Prototyped large set of runs on local Big Red 23 Teraflop supercomputer (OSCAR3 and modeling moving to CDK Gamess Jaguar) • Initial results discussed at conferences/workshops/papers • Gordon Conferences, ACS, SDSC tutorial • First new Cheminformatics courses offered • Advisory board set up and met – this is second meeting • Videoconferencing-based meetings with Peter Murray-Rust and group at Cambridge roughly every 2-3 weeks • Good or potentially good interactions with Local HTS in CGB, NIH DTP, Scripps, Lilly and Michigan ECCR

10. MLSCN Post-HTS Biology Decision Support Percent Inhibition or IC50 data is retrieved from HTS Grids can link data analysis ( e.g image processing developed in existing Grids), traditional Chem-informatics tools, as well as annotation tools (Semantic Web, del.icio.us) and enhance lead ID and SAR analysis A Grid of Grids linking collections of services atPubChem ECCR centers MLSCN centers Workflows encoding plate & control well statistics, distribution analysis, etc Question: Was this screen successful? Workflows encoding distribution analysis of screening results Question: What should the active/inactive cutoffs be? Question: What can we learn about the target protein or cell line from this screen? Workflows encoding statistical comparison of results to similar screens, docking of compounds into proteins to correlate binding, with activity, literature search of active compounds, etc Compounds submitted to PubChem PROCESS CHEMINFORMATICS GRIDS

11. Example HTS workflow: finding cell-protein relationships A protein implicated in tumor growth with known ligand is selected (in this case HSP90 taken from the PDB 1Y4 complex) The screening data from a cellular HTS assay is similarity searched for compounds with similar 2D structures to the ligand. Docking results and activity patterns fed into R services for building of activity models and correlations LeastSquares Regression RandomForests NeuralNets Similar structures are filtered for drugability, are converted to 3D, and are automatically passed to the OpenEye FRED docking program for docking into the target protein. Once docking is complete, the user visualizes the high-scoring docked structures in a portlet using the JMOL applet. Similar structures to the ligand can be browsed using client portlets.

12. 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 TeraGridSupercomputers“Flocks”

13. Methods Development at the CICC • Tagging methods for web-based annotation exploiting del.icio.us and Connotea • Development of QSAR model interpretability and applicability methods • RNN-Profiles for exploration of chemical spaces • VisualiSAR - SAR through visual analysis • See http://www.daylight.com/meetings/mug99/Wild/Mug99.html • Visual Similarity Matrices for High Volume Datasets • See http://www.osl.iu.edu/~chemuell/new/bioinformatics.php • Fast, accurate clustering using parallel Divisive K-means • Mapping of Natural Language queries to use cases and workflows • Advanced data mining models for drug discovery information

14. Structure of Proposal • a) Define audience that we are targeting • b) Cyberinfrastructure Framework with Key services -- Registry, Computing, portal, workflow • Exemplar Chemoinformatics Services • Exemplar workflows using services • Defined WSDL for key cases defined to allow others to contribute • Tutorial • c) Education • d) IT/Cyber-enhanced Computational Chemistry • e) Cheminformatics Research • Systems • Tools and Modeling

15. Questions • We expect to respond to “big” NIH RFP in about 4 months • Should we partner with Michigan? • Who is “customer” and how do we get more? • Do/Should chemists want our or more generally NIH’s product? • Interactions with “large” and “small” industry • What is balance between infrastructure, computational chemistry, Cheminformatics tools and research, chemical informatics systems and interfaces? • Should we stress literature (OSCAR3) project? • Balance of applications and generic capabilities? • How should we structure education component? • Field does not have strong student appeal compared to Bioinformatics • We are strong in Computer Sciences (Grids/Cyberinfrastructure) but doubtful if any CS reviewers • We are strong in Cheminformatics systems but not clear a recognized activity and how do we justify claim that Grids/Cyberinfrastructure/Open Access “good” • Should we link more with biology?

16. Covering our bases: Who are our “Customers”?

17. What do we need to conquer traditional chemical Research Community - High-Fidelity Structural Data, Redox Potentials, Spectroscopy, Transition State Structures, Energies, Molecular Orbitals…..

18. Application Scientists (Customers) Core group develops requirements for infrastructure and codes as services and tests infrastructure with key exemplar projects. Allow broad use by all Infrastructure/Technology Developers and Providers Build Cyberinfrastructure, design databases, workflow, support Web services with interface standards, wrap codes as services; Support infrastructure “Departments” of the future Center