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

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 GUIs RSS Feeds
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?
Univ. of Michigan
PkCell
Cambridge University
InChi generation / search
OSCAR
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
Visual Similarity Matrices for High Volume Datasets
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

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 at PubChem
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.
Least Squares
Regression
Random Forests
Neural Nets
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. Varuna environment for molecular modeling (Baik, IU)
Researcher
Chemical
Concepts
Papers
etc.
ChemBioGrid
Simulation Service FORTRAN Code,
Scripts
Experiments
DB Service Queries, Clustering, Curation, etc.
Reaction DB QM
Database
Condor
PubChem, PDB, NCI, etc.
QM/MM
Database
TeraGrid Supercomputers “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

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
Giving a class remotely to UM students with video and web conferencing

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. 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)