1. 1 http://www.chembiogrid.org Gary Wiggins for Geoffrey Fox April 30, 2007 Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 gcf@indiana.edu http://www.infomall.org

2. 2 Indiana University Focus Creating a comprehensive, easily accessible infrastructure for cheminformatics tools and data sources Becoming a central hub of cheminformatics education

3. 3 CICC Web Service Infrastructure Cheminformatics services Statistics services Database services Grid services Portal services

4. Web Services Vision Web services provide a neutral approach to exposing functionality They can be located anywhere: On your desktop Intranet Internet Literally anything can be made into a web service: Libraries Standalone programs Commerical code Open-source code

5. Modes of Access Web Pages Workflow Tools Taverna, Pipeline Pilot, Xbaya, etc. GUIs Chimera RSS Feeds Feeds include 2D/3D structures in CML Viewable in Bioclipse, Jmol as well as Sage etc. Two feeds currently available: SynSearch – get structures based on full or partial chemical names DockSearch – get best N structures for a target

6. Where Does Our Functionality Come From? Indiana University VOTables NCI DTP predictions Database services Cambridge University InChi generation / search OSCAR OpenEye Docking DigitalChemistry BCI fingerprints DivKMeans CDK Cheminformatics Univ. of Michigan PkCell R Foundation R package NIH PubChem PubMed gNova Consulting European Chemicals Bureau ToxTree toxicity predictions

7. 7 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 http://www.daylight.com/meetings/mug99/Wild/Mug99.html Visual Similarity Matrices for High Volume Datasets 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

8. Algorithm Development Goals Focus on interpretability and applicability Devise novel approaches to clustering problems Investigate the utility of low dimensional representations for a variety of problems Examples Ensemble feature selection (JCIM, in press) Cluster counting with R-NN curves (in revision)

9. Chemical Data Mining Working on screening data with Scripps, FL Random forests (modeling & feature selection) Naïve Bayes (modeling) Identifying features indicative of toxicity Domain applicability NCI DTP Cell line activity predictions Random forest models for 60 cell lines All available as downloadable R models web services (supply SMILES, get prediction) with web page clients

10. Computational Infrastructure R, CDK, and PubChem Goals Access cheminformatics from within R Access PubChem data from within R rcdk package allows to do cheminformatics within R using CDK functionality rpubchem provides access to PubChem compound data and bioassay data Searchable via assay ID, keywords J. Stat. Soft, 2007, 18(6)

11. 11 Example: R Statistics applied to PubChem data By exposing the R statistical package, and the Chemistry Development Kit (CDK) toolkit as web services and integrating them with PubChem, we can quickly and easily perform statistical analysis and virtual screening of PubChem assay data. Predictive models for particular screens are exposed as web services, and can be used either as simple web tools or integrated into other applications. Example below uses DTP Tumor Cell Line screens - a predictive model using Random Forests in R makes predictions of probability of activity across multiple cell lines (avail. at http://www.chembiogrid/cheminfo/ncidtp/dtp).

12. Databases Our databases aim to add value to PubChem or link into PubChem 3D structures (MMFF94) Searchable by CID, SMARTS, 3D similarity Docked ligands (FRED) 960,000 drug-like compounds into 7 targets Will eventually cover ~2000 targets

13. 13 Example: PubDock Database of 960K PubChem structures (the most drug-like) docked into proteins taken from the PDB Available as a web service, so structures can be accessed in your own programs, or using workflow tools like Pipeline Pilot Several interfaces developed, including one based on Chimera (below) which integrates the database with the PDB to allow browsing of compounds in different targets, or different compounds in the same target

14. How do we use all of this? Percent Inhibition or IC 50 data is retrieved from HTS Question: Was this screen successful? Question: What should the active/inactive cutoffs be? Question: What can we learn about the target protein or cell line from this screen? Compounds submitted to PubChem Workflows encoding distribution analysis of screening results 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 at PubChem ECCR centers MLSCN centers Workflows encoding plate & control well statistics, distribution analysis, etc 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 CHEMINFORMATICSPROCESS GRIDS

15. 15 Example HTS workflow: Finding cell- protein relationships A protein implicated in tumor growth with a known ligand is selected (in this case HSP90 taken from the PDB 1Y4 complex). Similar structures to the ligand can be browsed using client portlets. Once docking is complete, the user visualizes the high- scoring docked structures in a portlet using the JMOL applet. 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. The screening data from a cellular HTS assay is similarity searched for compounds with 2D structures similar to the ligand. Docking results and activity patterns fed into R services for building of activity models and correlations Least Squares Regression Random Forests Neural Nets

16. 16 Varuna environment for molecular modeling (Baik, IU) QM Database Researcher Simulation Service FORTRAN Code, Scripts Chemical Concepts Experiments QM/MM Database PubChem, PDB, NCI, etc. ChemBioGrid Reaction DB DB Service Queries, Clustering, Curation, etc. Papers etc. Condor TeraGrid Supercomputers “Flocks”

17. 17 Cheminformatics Education at IU School of Informatics degree programs: BS, MS, PhD Cheminformatics MS and track on PhD in Informatics Informatics Undergraduates can choose a chemistry cognate (minor in chemistry) Also Bioinformatics MS and Bioinformatics and Complex Systems tracks on PhD in Informatics Good employer interest but modest student understanding of value of Cheminformatics degree 3 core graduate courses in Cheminformatics plus seminars and independent study courses Significant interest in distance education versions of courses promising for the Graduate Certificate in Chemical Informatics http://www.informatics.indiana.edu

18. 18 Spreading cheminformatics education with distance education Partnered with the University of Michigan to offer our introductory graduate cheminformatics course at IU and Michigan as a CIC CourseShare UM pharmacy, chemistry and engineering students can be trained in cheminformatics for course credit at UM Individual students in academia, government, and small and large life science companies have taken the class remotely from all over the country for credit towards the graduate certificate Uses mixture of web conferencing (Breeze), videoconferencing, and online resources for maximum flexibility Most recent course wiki is available at http://cheminfo.informatics.indiana.edu/dj wild/I571_2006_wiki http://cheminfo.informatics.indiana.edu/dj wild/I571_2006_wiki Giving a class remotely to UM students with video and web conferencing

19. 19 CICC Infrastructure Vision Drug Discovery and other academic chemistry and pharmacology research will be aided by powerful modern information technology. ChemBioGrid is set up as distributed cyberinfrastructure in eScience model. ChemBioGrid will provide user interfaces (portals) 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 such as Digital Chemistry, OpenEye, and Google, and any user contributed programs. ChemBioGrid will define open interfaces to use for a particular type of service allowing plug and play choices between different implementations.

20. 20 CICC Senior Personnel Geoffrey C. Fox Mu-Hyun (Mookie) Baik Dennis B. Gannon Kevin E. Gilbert Rajarshi Guha Marlon Pierce Beth A. Plale Gary D. Wiggins David J. Wild Yuqing (Melanie) Wu Peter T. Cherbas Mehmet M. Dalkilic Charles H. Davis A. Keith Dunker Kelsey M. Forsythe John C. Huffman Malika Mahoui Daniel J. Mindiola Santiago D. Schnell William Scott Craig A. Stewart David R. Williams From Biology, Chemistry, Computer Science, Informatics at IU Bloomington and IUPUI (Indianapolis)