E Chemistry and Web 2 0

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E-Chemistry and Web 2.0

Marlon Pierce

mpierce@cs.indiana.edu Community Grids Lab

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One Talk, Two Projects

£ NIH funded Chemical Informatics and

Cyberinfrastructure

Collaboratory (CICC) @ IU.

¤ Geoffrey Fox

¤ Gary Wiggins

¤ Rajarshi Guha

¤ David Wild

¤ Mookie Baik

¤ Kevin Gilbert

¤ And others

£ Proposed Microsoft-Funded Project: E-Chemistry

¤ Carl Lagoze (Cornell),

¤ Lee Giles (PSU),

¤ Steve Bryant (NIH),

¤ Jeremy Frey (Soton),

¤ Peter Murray-Rust (Cambridge),

¤ Herbert Van de Sompel (Los Alamos),

¤ Geoffrey Fox (Indiana)

¤ And others

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CICC Infrastructure Vision

£ Chemical Informatics: drug discovery and other academic chemistry,

pharmacology, and bioinformatics research will be aided by powerful,

modern, open, information technology.

¤ NIH PubChem and PubMed provide unprecedented open, free data and

information.

¤ We need a corresponding open service architecture (i.e. avoid stove-piped

applications)

¤ CICC set up as distributed cyberinfrastructure in eScience model

£ Web clients (user interfaces) to distributed databases, results of high

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

¤ Composed of clients to open service APIs (mash-ups)

¤ Aggregated into portals

£ Web services manipulate this data and are combined into workflows.

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CICC Databases

£

Most of our databases aim to add value to

PubChem or link into PubChem

¤ 1D (SMILES) and 2D structures

£

3D structures (MMFF94)

¤ Searchable by CID, SMARTS, 3D similarity

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Docked ligands (FRED, Autodock)

¤ 906K drug-like compounds into 7 ligands

¤ Will eventually cover ~2000 targets

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Building Up the Infrastructure

£

Our SOA philosophy: use standard Web services.

¤ Mostly stateless

¤ Some cluster, HPC work needed but these populate databases

£

Services are aggregate-able into different

workflows.

¤ Taverna, Pipeline Pilot, …

£

You can also build lots of Web clients.

£

See

http:/

/www.chembiogrid.org/wiki/index.php/CICC_

Web_Resources for li

nks and details.

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Sample Services

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More Clients…

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Example: PubDock

£ Database of approximately 1 million 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 Polit

£ Several interfaces developed, including one based on Chimera (right) which integrates the

database with the PDB to allow browsing of compounds in

different targets, or different compounds in the same target

£ Can be used as a tool to help understand molecular basis of activity in cellular or image based assays

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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

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Example assay screening

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

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Relevance to Web 2.0

£

Some Web 2.0 Key Features

¤ REST Services

¤ Use of RSS/Atom feeds

¤ Client interfaces are “mashups”

¤ Gadgets, widgets for portals aggregate clients

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So…

¤ We provide RSS as an alternative WS format.

¤ We have experimented with RSS feeds, using Yahoo Pipes to manipulate multiple feeds.

¤ CICC Web interfaces can be easily wrapped as universal gadgets in iGoogle, Netvibes.

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RSS Feeds/REST Services

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Provide access to DB's via RSS feeds

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Feeds include 2D/3D structures in CML

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Viewable in Bioclipse, Jmol as well as Sage etc.

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Two feeds currently available

¤ SynSearch – get structures based on full or partial chemical names

¤ DockSearch – get best N structures for a target

£

Really hampered by size of DB and Postgres

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Tools and mashups based on web

service infrastructure

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Mining information from journal

articles

£ Until now SciFinder / CAS only chemistry-aware portal into journal information

£ We can access full text of journal articles online (with subscription)

£ ACS does not make full text available … but there are ways round that!

£ RSC is now marking up with SMILES and GO/Goldbook terms!

¤ www.projectprospect.org

£ Having SMILES or InChI means that we can build a

similarity/structure searchable database of papers: e.g. “find me all the papers published since 2000 which

contain a structure with >90% similarity to this one”

£ In the absence of full text, we can at least use the abstract

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Text Mining: OSCAR

£ A tool for shallow, chemistry-specific natural

language parsing of chemical documents (e.g. journal articles).

£ It identifies (or attempts to identify):

¤ Chemical names: singular nouns, plurals, verbs etc., also formulae and acronyms.

¤ Chemical data: Spectra, melting/boiling point, yield etc. in experimental sections.

¤ Other entities: Things like N(5)-C(3) and so on.

£ Part of the larger SciBorg effort

¤ See

http://www.cl.cam.ac.uk/~aac10/escience/sciborg.html)

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Mash-Up: What published compounds might bind to this protein? Create a database containing th

text of all recent PubMed abstract (2006-2007 = ~500,000)

Convert molecules to 3D and dock into a protein of interest

Visualize top docked molecules in a

Google-like interface

Use OSCAR to extract all of the chemical names referred to in the abstracts and covert to SMILES

DATABASE SERVICE

DOCKING SERVICE +

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E-Chemistry and Digital

Libraries

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E-Chemistry and Digital Libraries

£

Key problem with our SOA-based e-Science is

information management.

¤ Where is the service that I need?

¤ What does it do?

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We may consider our data-centric services to be

digital libraries.

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Data is diverse

¤ Documents

¤ Not just computational information like structures.

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Another point of view: how can I link together

publications, results, workflows, etc?

¤ That is, I need to manage digital documents.

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Digital Libraries

£ Open Archives Initiative Object Reuse and Exchange Project (OAI-ORE)

£ Developing standardized, interoperable, and machine-readable mechanisms to express information about compound information objects on the web.

£ Graph-based representations of connected digital objects.

£ Objects may be encoded in (for example) RDF or XML,

£ Retrievable via repositories with REST service interfaces (c.f. Atom Publishing Protocal)

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Challenges for E-Chemistry

£

Can digital library principals be applied to data as

well as documents?

¤ Can you link your workflow to your conference paper?

£

Can we engineer a publishing framework and

message formats around Web 2.0 principals?

¤ REST, Atom Publishing Protocol, Atom Syndication Format, JSON, Microformats

£

Can we do this securely?

¤ Access control, provenance, identify federation are key problems.

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More Information

£

Project Web Site:

www.chembiogrid.org

£

P

roject Wiki: w

ww.chembiogrid.org/wiki

£

Co

ntact me: mpierce@cs.indiana.edu

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CICC Combines Grid Computing with Chemical Informatics

CICC

Chemical Informatics and Cyberinfrastucture CollaboratoryFunded by the National Institutes of Health

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

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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

CHEMINFORMATICS

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Why?

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Need access to math and stat

functionality

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Did not want to recode algorithms

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Wanted latest methods

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Needed a distributed approach to

computation

¤

Keep computation on a powerful machine

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Why R?

£

Free, open-source

£

Many cutting edge methods avilable

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Flexible programming language

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Interfaces with many languages

¤

Python

¤

Perl

¤

Java

¤

C

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The R Server

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R can be run as a remote compute

server

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Requires the

rserve

package

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Allows authenticated access over

TCP/IP

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Connections can maintain state

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R as a Web Service

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On its own the R server is not a web

service

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We provide Java frontends to specific

functionalities

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The frontend classes are hosted in a

Tomcat web container

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Accessible via SOAP

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Full Javadocs for all available WS’s

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Functionality

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Two classes of functionality

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General functions

¥ Allows you to supply data and build a predictive model

¥ Sample from various distributions

¥ Obtain scatter plots and hisotgram

¥ Model development functions use a Java front-end to encapsulate model specific information

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Functionality

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Two classes of functionality

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Model deployment

¥ Allows you to build a model outside of the infrastructure

¥ Place the final model in the infrastructure

¥ Becomes available as a web service

¥ Each model deployed requires its own front end class

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Available Functionality

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Predictive models - OLS, RF, CNN,

LDA

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Clustering - k-means

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Statistical distributions

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XY plot and scatter plots

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Model deployment for single model

types and ensemble model types

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Deployed Models

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Since deployed models are visible as

web services we can build a simple web

front end for them

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Examples

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NC

I anti-cancer predictions

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Applications

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The R WS is not restricted to ‘atomic’

functionality

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Can write a whole R program

¤ Load it on the R compute server

¤ Provide a Java WS frontend

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Examples

¤ Feature selection

¤ Automated model generation

¤ Pharmacokinetic parameter calculation

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Data Input/Output

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Most modeling applications require data

matrices

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Depending on client language we can

use

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SOAP array of arrays (2D matrices)

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SOAP array (1D vector form of a 2D

matrix)

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Data Input/Output

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Some R web services can take a URL

to a VOTables document

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Conversion to R or Java matrices is done

by a local VOTables Java library

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R also has basic support for VOTables

directly

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Ignores binary data streams

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Interacting With R WS’s

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Traditional WS’s do not maintain state

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Predictive models are different

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A model is built at one time

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May be used for prediction at another time

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Need to maintain state

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State is maintained by serialization to R

binary files on the compute server

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Interacting with R WS’s

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Protocol

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Send data to model WS

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Get back model ID

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Get various information via model ID

¥ Fitted values

¥ Training statistics

¥ New predictions

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Cheminformatics at Indiana

University School of Informatics

David J. Wild

djwild@indiana.edu

Associate Director of Chemical Informatics & Assistant Professor

Indiana University School of Informatics, Bloomington

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Cheminformatics education at

Indiana

£ M.S. in Chemical Informatics

¤ 2 years, 36 semester hours

¤ Includes a 6-hour capstone / research project

¤ Opportunity to work in Laboratory Informatics (IUPUI) or closely with Bioinformatics (IUB)

¤ Currently 9 students enrolled

£ Ph.D. in Informatics, Cheminformatics Specialty

¤ 90 credit hours, including 30 hours dissertation research. Usually 4 years.

¤ Research rotations expose students to research in related areas

¤ Currently 4 students enrolled

£ Graduate Certificate

¤ 4 courses, all available by Distance Education

¥ I571 Chemical Information Technology

¥ I572 Computational Chemistry & Molecular Modeling

¥ I573 Programming for Science Informatics

¥ I553 Independent Study in Chemical Informatics

¤ D.E. students pay in-state fees! (~$800 per class)

£ See http://cheminfo.informatics.indiana.edu for more information, or a general review of cheminformatics education in Drug Discovery Today 11, 9&10 (May 2006), pp436-439

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Distance Education for

Cheminformatics

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Uses Breeze + teleconference for live sharing

of classes: all that is required is a P.C. and a

telephone. Optional Polycom

videoconferencing.

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Lectures are recorded for easy playback

through a web browser

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Wiki or similar webpage for dissemination of

course materials

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Also participate in CIC courseshare to give

class at University of Michigan

£

Of 75 students taking our courses since fall

2005, 39 have been D.E. students

£

See JCIM 2006; 46(2) pp 495 - 502 for more

details

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Current research in the Wild

lab

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Integration of cheminformatics tools and data

sources

¤ A web service infrastructure for cheminformatics

¤ Compound information & aggregation web service and interface (“by the way box”)

¤ An enhanced chatbot for exploting chemical information & web services

¤ A semantically-aware workflow tools for cheminformatics

¤ Data mining the NIH DTP tumor cell line database

¤ PubDock: a docking database for PubChem

£

Aggregating life science information from web

and journal documents

¤ Data mining semantically rich chemistry journal articles

¤ Document similarity based on chemical structure similarity

¤ Evaluating semantic markup of chemistry journal articles

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Integrating cheminformatics into the

chemistry lab

¤ Integrating cheminformatics with the Second Life virtual world

¤ Integrating cheminformatics tools with electronic lab notebooks

¤ Usability of cheminformatics tools

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Current research in the Guha

lab

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Predictive Modeling

¤ Interpretation, validation, domain applicability

¤ Generalization to other ‘models’ such as docking, pharmacophore etc

¤ Integration of multiple data types

¤ Addressing imbalanced and noisy datasets

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Analysis of Chemical Spaces

¤ Quantify distributions in spaces

¤ Investigation of density approaches

¤ Applications to lead hopping, model domains

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Methods to summarize & compare data

¤ Applications to HTS and smaller lead series type datasets

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Network models combining chemical

structures and biological systems

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Software and infrastructure

¤ Model exchange and annotation

¤ Pharmacophore representations, matching

¤ Toolkit development (CDK)

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Cheminformatics web service

infrastructure

Database Services

PostgreSQL + gNova

PubChem mirror (augmented)

Pub3D - 3D structures for PubChem

PubDock - Bound 3D structures

Compound-indexed journal article DB

NIH Human Tumor Cell Line

Local PubChem mirror VARUNA quantum

chemistry database

Statistics (based on R)

Regression, LDA

Neural Nets, Random Forest

K-means clustering Plotting

T-test and distribution sampling

Cheminformatics services

Docking (FRED)

3D structure generation (OMEGA)

Filtering (FRED, etc) OSCAR3

Fingerprints (BCI, CDK) Clustering (BCI)

Toxicity prediction (ToxTree)

R-based predictive models Similarity calculations

(CDK)

Descriptor calculation (CDK)

2D structure diagrams (CDK)

Xiao Dong, Kevin E. Gilbert, Rajarshi Guha, Randy Heiland, Jungkee Kim, Marlon E. Pierce, Geoffrey C. Fox and David J. Wild, Web service infrastructure for chemoinformatics, Journal of Chemical Information and Modeling, 2007;

47(4) pp 1303-1307

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RSC Project Prospect - what

can we do with the

information?

£

www.projectprospect.org

£

>

100 papers marked up with SMILES/InChI

(using OSCAR3), plus Gene Ontology and

Goldbook Ontology terms

£

Created similarity searchable PostgreSQL /

gNova database with paper DOIs, SMILES,

and ontology terms

£

Web service and simple HTML interfaces for

searching … “which papers reference

compounds similar to this one in the scope of

these ontological terms?”

£

Applying statistics to look at co-occurrence of

compounds, structural features (MACCS

keys) and ontological terms in papers

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Greasemonkey / OSCAR

script

http://cheminfo.informatics.indiana.edu:8080/ChemGM/index.jsp

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By the way… annotation

(mock-up!)

By the way…

This compounds is very similar to a prescription drug,Tamoxifen.

This compound is referenced in20 journal articlespublished in the last 5 years Similar compounds are associated with the words “toxic” and “death” in280 web pages

It appears to be covered under3 patents

It has been shown to be active in5 screens

Computer models predict it to show some activity against8 protein targets

Here are some comments on this compound:

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Some useful chemical reactions

IodoacetateaIodoacetamideI-CH4COO- ICH2CONH2

This may also react, chem favored byalkaline pH

….

Cheminformatics aware

simple lab notebook (mock

up!)

Free text input can be converted to machine

readable form by electrovaya

Automatic detection o data fields (yield, etc)

Where possible Plug-in allows structures

to be drawn with the pen and cleaned up

Web service interfac provides access to computation and searching.

Page is marked up by what is possible

FIND INFO ABOUT THIS REACTION

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Automatic workflow

generation and natural

language queries

£

Develop service ontology using OWL-S or

similar language

¤ Allows service interoperability, replacement and input/outut compatibility

£

We can then use generic reasoning and

network analysis tools to find paths from

inputs to desired outputs

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Natural language can be parsed to inputs and

desired outputs

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Smart Clients <--> Agents <--> Services

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Possible “supercharged life science Google?”

- e.g. type in “what compounds might bind to

the enclosed protein?”

2D -> 3D 2 struct ur crawl er dock 3D searc h P’pho r searc h 2 simila rity 2D structures

2D structures 3D structures

3D structures 3D structures & complexes

dock = bind

3D protei structure

result

3D structures are compounds 2D structures are

compounds

3D structures are compounds

Figure

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References

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