David Wild, Sheffield Conference, June 2007. Page 1 Indiana University School of

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A Web Service Infrastructure for Chem[o]informatics

presented at the 4th Joint Sheffield Conference on Chemoinformatics, June 2007

David J. Wilddjwild@indiana.edu

Assistant ProfessorIndiana University School of Informatics, Bloomington

http://djwild.info

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Overview

• Chem[o]informatics at Indiana University• The web service infrastructure• Examples of use

– Mashups and web interfaces– Workflows– Complex querying of journal articles

– Greasemonkey scripts

• Looking further in the future

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Chem[o]informatics at Indiana University

• Derived from long standing courses in Chemical Information Handling startedby Gary Wiggins

• Moved to School of Informatics in 2000• Boosted over the last few years through success of the School of Informatics

and by NIH Cheminformatics funding (www.chembiogrid.org)• M.S., Ph.D. and graduate certificate in cheminformatics• Graduate Certificate through Distance Education• Research partnership with Community grids lab• More information:

– http://cheminfo.informatics.indiana.edu– http://www.chembiogrid.org

– Education• JCIM 2006; 46(2) pp 495 - 502• Drug Discovery Today 11, 9&10 (May 2006), pp436-439

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Developments in the Web World

• Semantic Web / Web 2.0 – “Next Big Thing”– Live computation on the web

• Web services, API’s - e.g. Google Maps (http://www.google.com/apis/)• Mash-ups and workflows that use these services - e.g.

http://www.programmableweb.com, http://pipes.yahoo.com

– Social computing• Social networks - Facebook, Myspace, Linkedin• Information sharing - wikis, blogs, folksonomies, etc

– Description of meaning as well as content of information• Ontology languages, automated reasoning• Semantic interoperability of services and information

• Well funded– eScience (UK): £200m over 2001-2006 period (http://www.rcuk.ac.uk/escience/– http://www.mygrid.org.uk/ )– cyberinfrastructure / grid (US): NIH Molecular Libraries Initiative,

http://nihroadmap.nih.gov/molecularlibraries/, NSF cyberinfrastructure

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Web Services

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Chemical Informatics web service infrastructure

• Database Services– Local NIH DTP Human Tumor

Cell Line set

– Local PubChem mirror– Derived properties database– Pub3D, PubDock

– Synonym service– VARUNA quantum chemistry

database• Statistics (based on R)

– Regression, Neural Nets, RandomForest

– LDA– K-means clustering– Plotting– T-test and distribution sampling

• Computation Services– OpenEye FRED, OMEGA,

FILTER, …

– Cambridge OSCAR3– BCI fingerprint generation,

Ward’s, Divisive K-meansclustering

– Tox Tree– Similarity & fingerprint

calculations (CDK)– Descriptor calculation (CDK)

– 2D structure diagrams (CDK)– 2D->3D File format conversions

www.chembiogrid.org

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The world of Web 2.0 and mash-ups

• Web 2.0 and resulting mashups, etc., are further blurring the boundaries andpopularizing the "best bits" of complex subdisciplines: e.g. GIS -> Google Maps -> lots of mashups (770! - seehttp://www.programmableweb.com/api/GoogleMaps/mashups)

• We can imagine the same happening soon for chemoinformatics (e.g.structure, substructure searching) and bioinformatics (homology modeling etc).

• For more information see http://www.programmableweb.com/ andhttp://web2.wsj2.com/

• So the first stage is building applications (“mash-ups”) that use web servicesfrom one or more disciplines

• Then we start doing some really interesting stuff!

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Mashups - Google Maps + Estate Agent db (www.housingmaps.com)

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PubChemSR - .NET app for searching PubChem

Available from http://darwin.informatics.indiana.edu/juhur/Tools/PubChemSR/

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PubDock - database of docked PubChem Ligands

• 1 million PubChem compounds (drugable) docked into PDB proteins (currently7 but more coming)

• Two interfaces - web and standalone• This is really a bioinformatics / chemoinformatics mashup• Retrieve top hits for a protein• Organize proteins by similarity between docking profiles over compounds• Cluster compounds by docking profile across cluster targets• Uses many web services: PDB services, our PubDock database service, our

CDK services etc…

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PubDock - Chimera-based interface

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Prediction of activity against 40 tumor cell lines

http://rguha.ath.cx/~rguha/ncidtp/dtp

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Results for Gleevec

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Quick and easy NLP - Kemo, a chatbot for PubChem

http://cheminfo.informatics.indiana.edu:8080/kemo

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Workflows - Taverna (taverna.sourceforge.net)

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Workflow in Xbaya - a meteorology tool!

http://www.extreme.indiana.edu/xgws/xbaya/

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Workflow in .NET

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Automatic name detection and structure generation (batch)• OSCAR3 - Murray Rust Group

– A tool for shallow, chemistry-specific natural language parsing of chemicaldocuments (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)

– http://wwmm.ch.cam.ac.uk/wikis/wwmm/index.php/Oscar3• Lexichem - OpenEye

– Toolkit for conversion of chemical structure names (IUPAC, traditional) toconnection tables, SMILES, InChI, etc.

– Used by Reel Two (www.reeltwo.com) in their SureChem package for searchingpatents based on chemical structures

– http://www.eyesopen.com/products/toolkits/lexichem.html• ACD/Name to Structure

– Batch conversion of chemical structure names to and from InChIs and SMILES– http://www.acdlabs.com/products/name_lab/rename/batch.html

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Harvesting example - a database of abstracts indexed by SMILES

• As proof of concept, ran OSCAR3 on 1 year’s worth of PubMed abstracts(2005-2006) to extract chemical structure names, convert them to SMILES, andindex the abstracts by SMILES

• Stored in a PostgreSQL database with gNova CHORD for structure andsimilarity searching

• Potential for use as a way of detecting new trends in publication as well as forpublication alerts based on substructure or similarity

• 208,141 unique abstracts• 10,468 chemical structure names identified by OSCAR3• 6,560 unique SMILES (6448 unique InChIs)• 3,185 of these have PubChem entries• Of 10,000 compounds randomly selected from PubChem, 2,500 compounds

had names (synonyms) found in the text of the PubMed abstracts• Ratio of mean number of names in abstracts to papers - 4.172 : 36.67• In comparison to a random 10,000 compound subset of PubChem, 84% passed

the Lipinski Rule of 5 vs 73% for PubChem. Passing the OpenEye Filter wascloser (13% vs 15%).

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Workflow / mash-up of PubMed abstracts and docking

Create a database containing the

text of all recent PubMed abstracts

(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

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Marking up chemical structures in web pages using Greasemonkey

http://chem-bla-ics.blogspot.com/2006/12/smiles-cas-and-inchi-in-blogs.html

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Live PDB links and greasemonkey paper -> blog entry link

http://www.redbrick.dcu.ie/~noel/PDB/findPDB.html

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Greasemonkey / OSCAR script

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

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PubChem - view 3D structure greasemonkey script

http://rna.informatics.indiana.edu/hgopalak/download_Jscript.html

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Smart mining of drug discovery information

• For scientists, it’s usually more appropriate to think in terms of informationthan tools

• Many information questions of interest to scientists are conceptually simple butcomplex to implement, not necessarily mapping onto individualchemoinformatics algorithms

• Whilst some information needs are recurring and constant,many are unique or rapidly changing

• The gap between what theoretically could be done computationally, and what isdone, is currently rather large,for a variety of reasons

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Simple questions can be complex to answer…

Oracle Database (HTS)

Compounds were tested

against related assays and

showed activity, including

selectivity within target families

Oracle Database (Genomics)

? None of these compounds

have been tested in a microarray

assay

Computation

The information in the

structures and known activity

data is good enough to create a

QSAR model with a confidence

of 75%

External Database (Patent)

Some structures with a

similarity > 0.75 to these appear

to be covered by a patent held by

a competitor

Computation

All the compounds pass the

Lipinksi Rule of Five and toxicity

filters

Excel Spreadsheet (Toxicity)

One of the compounds was

previously tested for toxicology

and was found to have no liver

toxicity

Word Document (Chemistry)

Several of the compounds had

been followed up in a previous

project, and solubility problems

prevented further development

Journal Article

A recent journal article

reported the effectiveness of

some compounds in a related

series against a target in the

same family

Word Document (Marketing)

A report by a team in

Marketing casts doubt on

whether the market for this target

is big enough to make

development cost-effective

SCIENTIST

“These compounds look promising from their

HTS results. Should I commit some chemistry

resources to following them up?”

?

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Supercharged Life Science Google (mock up!)

what compounds might bind to the enclosed protein?

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By the way… annotation (mock-up!)By the way…

This compounds is very similar to aprescription drug, Tamoxifen.

This compound is referenced in 20 journalarticles published in the last 5 years

Similar compounds are associated with thewords “toxic” and “death” in 280 web pages

It appears to be covered under 3 patents

It has been shown to be active in 5 screens

Computer models predict it to show someactivity against 8 protein targets

Here are some comments on this compound:

David Wild: don’t take any notice of thecomputational models - they are rubbish

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Indexing the world’s chemical information AND functionality

• Expose databases and computational functionality as web services– Wrap as much computational capability as we can as web services– Have databases accessible in a standard way (PostgreSQL / gNova CHORD)– Make it easy to access (c.f. Google Maps API)– Innovate with mash-ups

• Crawl and index web pages, journal articles, etc. for structures (InChIs, SMILES), images(converted using Clide or ChemReader), names (converted using OSCAR3 or similarpackage, other information (IR spectra, reactions, etc…)– pull information into searchable databases– tag or annotate in situ– federate with existing databases (PubChem etc)

• Now we know what information we have, and what we can do with it, develop cleverfront ends to do useful things– workflows and mashups– “by the way” annotations– natural language interfaces– ontologies and reasoning tools (unless something else works better!)

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OWL-S Web Ontology Language

• Profile– Describe what a service

does (semantically)

• Process model– Detailed description of a

service's operation

• Grounding– How to get access to the

service

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Generation of OWL-S in Protégé

http://owlseditor.semwebcentral.org

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Matching query input with OWL-S

• Still working on this!• Can easily NLP straightforward queries. Harder part is mapping onto

ontologies• Suppose the user query is submitted as the following

– “Find the drug-like compounds similar to compound X from PubChem that have aTanimoto coefficient value higher than 0.7”

• Description Logic construct is asserted as following:

SimilaritySearch DatabaseSearchServiceIHasInput.2DstructureIHasInput.TanimotoCoefficient

IHasInput.2IHasOutput.2DstructureSetIHasOutput.1

Filter OpeneyeSoftwareIHasInput.2DstructureSetIHasInput.1

IHasOutput.DruglikeCompoundsIHasOutput.1

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Summary

• Web services allow us to expose data and computational ability in standardways so that they can be used in association with other methods (incheminformatics or elsewhere)

• It’s all part of a wider web development, which is still immature but isundoubtably the way things are going (on the web at least)

• Semantic Web / Web 2.0 are still rather different• Mashups, workflows and automated reasoning tools offer the possibility of

better mapping techniques and data to real scientific information needs in amanner which is straightforward for the scientists

• Power is increased when structural information can be mined from journalarticles and other text documents

• But, we have to worry about reliability, critical dependency, applicability, andinterpretability

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Orac & Slave: Blakes Seven

Orac is a highly advanced supercomputerdeveloped by the scientist Ensor. It is

extremely terse and short-tempered. Orac hasthe ability to communicate with all other

computers that carry tarriel cells and henceprovide the Liberator crew with valuable

knowledge. Through calculation of probability,Orac can predict the future. Orac's systems are

multi-dimensional; it projects a carrier beamthrough the same dimension that allows

telepaths to transfer thoughts.

Slave is the master computer on the group's secondvessel, the Scorpio, programmed with a particularly

servile personality

taken from wikipedia

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Acknowledgements

• My research group: Rajarshi Guha, Xiao Dong, David Jiao, Junguk Hur, HariniGopalakrishnan, Huijun Wang

• Marlon Pierce, Randy Heiland, Jake Kim• Gary Wiggins & Geoffrey Fox• Peter Murray Rust, Peter Corbett (Cambridge)• Funding:

– NIH Exploratory Centers for Cheminformatics Research grant

– Microsoft Smart Clients for eScience grant