2011 ebi industry workshop

1 2011-EBI-Industry-SW::Dumontier

Predicting Druglikeness and Toxicity from Integrated Data and Services on

the Life Science Semantic Web

Michel Dumontier, Ph.D.

Associate Professor of Bioinformatics, Department of Biology, School of Computer Science, Institute of Biochemistry, Carleton University

Professeur Associé, Département d’informatique et de génielogiciel, Université Laval

Ottawa Institute of Systems BiologyOttawa-Carleton Institute of Biomedical Engineering


Is caffeine a drug-like molecule?

Is acetaminophen toxic?


Finding the right information to answer a question is hardand sometimes requires a sophisticated workflow



What if we could answer a question by automatically building a knowledge base

using both data and services?


The Semantic Web is a web of knowledge.

It is about standards for publishing, sharing and querying knowledge drawn from diverse sources

It enables the answering of sophisticated questions


To answer this question we need to know:

• what ‘drug like molecule’ really means• caffeine’s molecular structure• the ability to compute the relevant attributes• determine whether caffeine satisfies the requirements of being ‘drug like’

Is caffeine a drug-like molecule?


Lipinski Rule of Five

• Rule of thumb for druglikeness (orally active in humans)(4 rules with multiples of 5)– mass of less than 500 Daltons– fewer than 5 hydrogen bond donors– fewer than 10 hydrogen bond acceptors– A partition coefficient value between -5 and 5

We need a more formal (machine understandable) description of a ‘drug-like molecule’ which specifies values for chemical descriptors


ontology as a strategy to

formally represent knowledge


The Web Ontology Language (OWL) Has Explicit Semantics

Can therefore be used to capture knowledge in a machine understandable way


Semanticscience Integrated Ontology (SIO)

• OWL2 ontology• 900+ classes covering basic types (physical, processual, abstract,

informational) with an emphasis on biological entities• 169 basic relations (mereological, participatory, attribute/quality,

spatial, temporal and representational)• axioms can be used by reasoners to generate inferences for

consistency checking, classification and answering questions about life science knowledge

• embodies emerging ontology design patterns – specifies the representation of knowledge

• dereferenceable URIs• searchable in the NCBO bioportal• Available at http://semanticscience.org/ontology/sio.owl

http://semanticscience.org/ontology/sio.owl


2011-EBI-Industry-SW::Dumontier

The Chemical Information Ontology (CHEMINF)

• 100+ chemical descriptors• 50+ chemical qualities• Relates descriptors to their

specifications, the software that generated them (along with the running parameters, and the algorithms that they implement)

• Contributors: Nico Adams, Leonid Chepelev, Michel Dumontier, Janna Hastings, Egon Willighagen, Peter Murray-Rust, Cristoph Steinbeck

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http://semanticchemistry.googlecode.com


Molecular structure can be represented using a SMILES string, which is a common representation

of the chemical graph

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ball & stick model for caffeine

SMILES string for caffeine

Cn1cnc2n(C)c(=O)n(C)c(=O)c12


Lipinski Rule of Five• Empirically derived ruleset for druglikeness

(4 rules with multiples of 5)– mass of less than 500 Daltons– fewer than 5 hydrogen bond donors– fewer than 10 hydrogen bond acceptors– A partition coefficient value between -5 and 5

• A formal description using OWL:


What we then need are services that will consume SMILES strings and annotate the molecule with the required chemical

descriptors

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then we can reason about whether it satisfies the drug-likeness definition


Semantic Automated Discovery and Integration

http://sadiframework.org

Mark Wilkinson, UBCMichel Dumontier, Carleton UniversityChristopher Baker, UNB

SADI is a framework to create Semantic Web services using OWL classes as service inputs and outputs

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http://sadiframework.org/


Create code stubs using the ontology

• Publish the ontology to a web-accessible locationhttp://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl

• Make sure that the class names are resolvable(easy when using the hash notation)

http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#smiles-moleculehttp://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#logp-moleculehttp://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#hbdc-moleculehttp://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#hdba-moleculehttp://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#lipinksi-druglike-molecule

• Download/checkout the codehttp://sadiframework.org

• Run the code generator (Java, Perl, python)– specify the URIs that correspond to input and output types

• Implement the functionality– We used the Chemistry Development Kit (CDK) to implement 4 services

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Responds to a GET operation by providing the service description in RDF

conforms to Feta (BioMoby, myGrid)

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curl http://cbrass.biordf.net/logpdc/logpc

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:j.0="http://www.mygrid.org.uk/mygrid-moby-service#" > <rdf:Description rdf:about=""> <j.0:hasServiceDescriptionText>no description</j.0:hasServiceDescriptionText> <j.0:hasServiceNameText rdf:datatype="http://www.w3.org/2001/XMLSchema#string">logpc</j.0:hasServiceNameText> <j.0:hasOperation rdf:resource="#operation"/> <rdf:type rdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#serviceDescription"/> </rdf:Description> <rdf:Description rdf:about="#input"> <j.0:objectType rdf:resource="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#smilesmolecule"/> <rdf:type rdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#parameter"/> </rdf:Description> <rdf:Description rdf:about="#operation"> <j.0:outputParameter rdf:resource="#output"/> <j.0:inputParameter rdf:resource="#input"/> <rdf:type rdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#operation"/> </rdf:Description> <rdf:Description rdf:about="#output"> <j.0:objectType rdf:resource="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#alogpsmilesmolecule"/> <rdf:type rdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#parameter"/> </rdf:Description></rdf:RDF>


Responds to a POST containing service input with a service output in RDF

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<rdf:Description rdf:about="http://semanticscience.org/sadi/ontology/caffeine.rdf#mdalogp"> <rdf:type rdf:resource="http://semanticscience.org/resource/CHEMINF_000251"/> <j.0:SIO_000300 rdf:datatype="http://www.w3.org/2001/XMLSchema#double">-0.4311000000000006</j.0:SIO_000300> </rdf:Description>

<rdf:RDF xmlns="http://semanticscience.org/sadi/ontology/caffeine.rdf#" xmlns:so="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:sio="http://semanticscience.org/resource/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#"> <so:smilesmolecule rdf:about="http://semanticscience.org/sadi/ontology/caffeine.rdf#m"> <sio:SIO_000008 rdf:resource = "http://semanticscience.org/sadi/ontology/caffeine.rdf#msmiles"/> </so:smilesmolecule> <sio:CHEMINF_000018 rdf:about = "http://semanticscience.org/sadi/ontology/caffeine.rdf#msmiles"> <sio:SIO_000300 rdf:datatype="xsd:string">Cn1cnc2n(C)c(=O)n(C)c(=O)c12</sio:SIO_000300> </sio:CHEMINF_000018></rdf:RDF>

The response is in RDF:

The query is in RDF:


61 Chemical Semantic Web Services

• these and an increasing number of semantic web services are registered at http://sadiframework.org/registry/services/

http://sadiframework.org/registry/services/


Now what?

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2011-EBI-Industry-SW::Dumontier23

Semantic Health and Research Environment

SHARE is an application that execute (SPARQL) queries as workflows over SADI Services


“Reckoning”

dynamic discovery of instances of OWL classes through synthesis and invocation of a Web Service workflow capable of generating data described by the OWL class restrictions, followed by reasoning to classify the data

into that ontology

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ChEBI publishes (non-SW) data!

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Bio2RDF provides ChEBI in RDF

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Bio2RDF covers the major biological databases

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Bio2RDF’s RDFized data fits together

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Resource Description Framework (RDF)

Uniform Resource Identifier (URI) can be used as entity names

Bio2RDF specifies the naming convention

http://bio2rdf.org/uniprot:P05067

is a name for Amyloid precursor protein

http://bio2rdf.org/omim:104300

is a name for Alzheimer disease

uniprot:P05067

omim:104300

Allows one to talk about anything

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Life Science Dataset Registry Coordinates Naming

• Provides stable URI patterns for records and the entities they describe.

Directory Service• ~1500 datasets & dozens of resolvers.

Discovery Service• Registry links entities to records and their representations (RDF/XML,

HTML, etc) and provider (Bio2RDF, Uniprot)

Redirection Service• Automatic redirection to data provider document

Stanford : 22-04-2010


Bio2RDF is now serving over 40 billion triples of linked biological data

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Bio2RDF is a framework to create and provision linked data networks

Francois Belleau, Laval UniversityMarc-Alexandre Nolin, Laval University

Peter Ansell, Queensland University of TechnologyMichel Dumontier, Carleton University


Bio2RDF is part of a growing web of linked data

“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”


something you can lookup or search for with rich descriptions


SPARQL is the new cool kid on the query block

SQL SPARQL


Query for log p

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Query: Is caffeine a drug-like molecule?

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Benefits

• Data remains distributed – as the internet was meant to be!

• Data is not “exposed” as a SPARQL endpoint– greater provider-control over computational resources

• Service invocation is straightforward and matchmaking by reasoning about ontology-based input/output descriptions

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Is acetaminophen toxic?

• Classical approaches involve decision trees or machine learning over validated data.

• Algorithms are often proprietary, even by the regulatory agencies

• Issues around which data was used, and what the informative parameters are, and how easily can new information affect the outcomes?


OWLED2011 : Large-Scale Boolean Feature Based Trees as OWL ontologies


DL Reasoners give Explanations


Summary

• Semantic Web technologies offer tantalizing ability to create and share data and services for drug discovery– Bio2RDF provides linked life science data– SADI provides a framework to provide semantic web

services– SHARE allows us to simultaneously query and reason

about data and services represented using RDF/OWL– Expressive ontologies can be used to make toxicity

decisions transparent


Acknowledgements

Bio2RDF: Peter Ansell, Francois Belleau, Allison Callahan, Jacques Corbeil, Jose Cruz-Toledo, Alex De Leon, Steve Etlinger, James Hogan, Nichealla Keath, Jean Morissette, Marc-Alexandre Nolin, Nicole Tourigny, Philippe Rigault and, Paul Roe

SADI: Christopher Baker, Melanie Courtot, Jose Cruz-Toledo, Steve Etlinger, Nichealla Keath, Artjom Klein, Luke McCarthy, Silvane Paixao, Ben Vandervalk, Natalia Villanueva-Rosales, Mark Wilkinson

CHEMINF GroupLeo ChepelevJanna HastingsEgon WillighagenNico Adams

Toxicity GroupLeo ChepelevDana Klassen


[email protected]

Website: http://dumontierlab.com Presentations: http://slideshare.com/micheldumontier

http://dumontierlab.com/

http://slideshare.com/micheldumontier

Health & Medicine

2011 ebi industry workshop