Overview of Chemical Informatics and Cyberinfrastructure Collaboratory

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@indi

ana.edu

Activities

n 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

n 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

n Also have started an activity to identify “customers” for

Cyberinfrastructure and its implied Chemistry eScience model

CICC Senior Personnel

n

Geoffrey C. Fox

n

Mu-Hyun (Mookie) Baik

Dennis B. Gannon

n

Marlon Pierce

Beth A. Plale

n

Gary D. Wiggins

David J. Wild

n

Yuqing (Melanie) Wu

n

Peter T. Cherbas

Mehmet M. Dalkilic

Charles H. Davis

n

A. Keith Dunker

Kelsey M. Forsythe

Kevin E. Gilbert

John C. Huffman

Malika Mahoui

Daniel J. Mindiola

Santiago D. Schnell

William Scott

n

Craig A. Stewart

David R. Williams

From Biology, Chemistry, Computer Science, Informatics

at IU Bloomington and IUPUI (Indianapolis)

CICC Infrastructure Vision

n Drug Discovery and other academic chemistry and pharmacology

research will be aided by powerful modern information technology

ChemBioGrid set up as distributed cyberinfrastructure in eScience model

n ChemBioGrid will provide portals (user interfaces) to distributed

databases, results of high throughput screening instruments, results of computational chemical simulations and other analyses

n ChemBioGrid will provide services to manipulate this data and combine in

workflows; it will have convenient ways to submit and manage multiple jobs

n ChemBioGrid will include access to PubChem, PubMed, PubMed Central,

the Internet and its derivatives like Microsoft Academic Live and Google Scholar

n The services include open-source software like CDK, commercial code from

vendors from BCI, OpenEye, Gaussian and Google, and any user contributed programs

n ChemBioGrid will define open interfaces to use for a particular type of

service allowing plug and play choice between different implementations

CICC Combines Grid Computing with Chemical Informatics

CICC

CICC

www.chembiogrid.org

Indiana University Department of Chemistry, School of Informatics, and Pervasive Technology Laboratories

Science and

Cyberinfrastructure

.

Large Scale Computing Challenges

Chemical Informatics is non-traditional area of high performance computing, but many new, challenging problems may be investigated.

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.

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 PubMed DataBas e OSCAR Text Analysis POVRay Parallel Renderin g Initial 3D Structure Calculatio n Toxicity Filtering Cluster Groupin g Docking Molecular Mechanic s Calculatio ns Quantum Mechanics Calculatio ns IU’s Varuna DataBase NIH PubChe m DataBase Chemical informatics text analysis programs can process 100,000’s of abstracts of online journal articles to extract chemical signatures of potential drugs.

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.

Big Red (and the TeraGrid) will also enable us to perform time consuming, multi-stepped Quantum Chemistry

calculations onallof PubMed. Results go back to public databases that are freely accessible by the scientific community.

CICC Prototype Web Services

Molecular weights Molecular formulae Tanimoto similarity 2D Structure diagrams Molecular descriptors 3D structures

InChI

generation/search CMLRSS

R and Excel

Basic cheminformatics

Application based services

Compare (NIH)

Toxicity predictions (ToxTree) Literature extraction (OSCAR3) Clustering (BCI Toolkit)

Docking, filtering, ... (OpenEye Varuna simulation

 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

Next steps? Key Ideas

 Add value to PubChem with additional distributed service

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

Web Service Locations

Indiana University

 Clustering  VOTables

 OSCAR3

 Toxicity classification  Database services

Penn State University (now moved to IU)

CDK based services

 Fingerprints

 Similarity calculations  2D structure diagrams  Molecular descriptors

Cambridge University

 InChI generation / search

 CMLRSS

 OpenBabel

InfoChem

 SPRESI

database

SDS

Typical

TeraGrid Site

NIH

Cheminformatics Education at IU

n Linked to bioinformatics in Indiana University’s School of Informatics

• School of Informatics degree programs BS, MS, PhD

n Programs offered at both the Indianapolis (IUPUI) and Bloomington

(IUB) campuses

• Bioinformatics MS and track on PhD

• Chemical Informatics MS and track on PhD

• Informatics Undergraduates can choose a chemistry cognate (change

to Life Sciences )

n 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!

n Good employer interest but modest student understanding of value of

Cheminformatics degree

n 3 core courses in Cheminformatics plus seminar/independent studies

n Significant interest in distance education version of introductory

Cheminformatics course (enrollment promising in Distance Graduate

Certificate in Chemical Informatics)

Current Status

n Web site http://www.chembiogrid.org

n Wiki chosen to support project as a shared editable web space

n Building Collaboratory involving PubChem – Global Information System

accessible anywhere and at any time – enhance PubChem with distributed

tools (clustering, simulation, annotation etc.) and data

n Adopted Taverna as workflow as popular in Bioinformatics but we will

evaluate other systems such as GPEL from LEAD

n Demonstrated CI-enhanced Chemistry simulations

n Initiated Data-mining, User interface and Chemical Informatics tools

research

n Prototyped large set of runs on local Big Red 23 Teraflop supercomputer

(OSCAR3 and modeling moving to CDK Gamess Jaguar)

n Initial results discussed at conferences/workshops/papers

• Gordon Conferences, ACS, SDSC tutorial

n First new Cheminformatics courses offered

n Advisory board set up and met – this is second meeting

n Videoconferencing-based meetings with Peter Murray-Rust and group at

Cambridge roughly every 2-3 weeks

n Good or potentially good interactions with Local HTS in CGB, NIH DTP,

Scripps, Lilly and Michigan ECCR

MLSCN Post-HTS Biology Decision Support

Percent Inhibition or IC50 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 a

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

CHEMINFORMATIC S

PROCES

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)

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 similar 2D structures to the ligand.

Docking results and activity patterns fed into R services for building of activity models and correlations Leas Squares Regression Rando

Forests NeuraNets

Varuna

environment for molecular modeling (Baik,

IU)

QM Database

Researcher

Simulation Servic

FORTRAN Code, Scripts

Chemical Concepts

Experime nts

QM/MM Database PubChem, PDB

NCI, etc.

ChemBioGrid

Reactio DB

DB Servic Queries, Clustering

Curation, etc.

Papers etc.

Condor

TeraGri

Methods Development at the CICC

n Tagging methods for web-based annotation exploiting del.icio.us

and Connotea

n Development of QSAR model interpretability and applicability

methods

n RNN-Profiles for exploration of chemical spaces n VisualiSAR - SAR through visual analysis

¨ See http://www.daylight.com/meetings/mug99/Wild/Mug99.html

n Visual Similarity Matrices for High Volume Datasets

¨ See http://www.osl.iu.edu/~chemuell/new/bioinformatics.php

n Fast, accurate clustering using parallel Divisive K-means

n Mapping of Natural Language queries to use cases and workflows n Advanced data mining models for drug discovery information

Structure of Proposal

n

a) Define audience that we are targeting

n

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

n

c) Education

n

d) IT/Cyber-enhanced Computational Chemistry

e) Cheminformatics Research

•

Systems

•

Tools and Modeling

Questions

n

We expect to respond to “big” NIH RFP in about 4 months

n

Should we partner with Michigan?

n

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

n

What is balance between infrastructure, computational

chemistry, Cheminformatics tools and research, chemical

informatics systems and interfaces?

n

Should we stress literature (OSCAR3) project?

n

Balance of applications and generic capabilities?

n

How should we structure education component?

• Field does not have strong student appeal compared to Bioinformatics

n

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”

n

Should we link more with biology?

Covering our bases: Who are our “Customers”?

What do we need to conquer traditional chemical Research Community

- High-Fidelity Structural Data, Redox Potentials, Spectroscopy, Transition State Structures, Energies, Molecular Orbitals…..

“Departments” of the future Center

Infrastructure/Technology Developers and Providers

Build Cyberinfrastructure, design databases, workflow, support Web services with interface standards, wrap codes as services;

Support infrastructure

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