chembiogrid Gary Wiggins for Geoffrey Fox April 30, 2007

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

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

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. 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. 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 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

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). Docking results and activity patterns fed into R services for building of activity models and correlations The screening data from a cellular HTS assay is similarity searched for compounds with 2D structures similar to the ligand. 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.

16. Varunaenvironment for molecular modeling (Baik, IU) Researcher Chemical Concepts Papers etc. ChemBioGrid Simulation ServiceFORTRAN Code, Scripts Experiments DB ServiceQueries, Clustering,Curation, etc. ReactionDB QM Database Condor PubChem, PDB,NCI, etc. QM/MM Database TeraGridSupercomputers“Flocks”

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. 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/djwild/I571_2006_wiki Giving a class remotely to UM students with video and web conferencing

19. CICC Infrastructure Vision • Drug Discovery and other academic chemistry and pharmacologyresearch 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. CICC Senior Personnel • 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 • 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 From Biology, Chemistry, Computer Science, Informatics at IU Bloomington and IUPUI (Indianapolis)