ACS RDF Symposium, Boston, August 2010

The distribution of computational resources as web services and their execution as workflows has enabled facile computation and data integration for bio− and cheminformatics. The Semantic Automated Discovery and Integration (SADI) framework addresses many shortcomings of similar frameworks, such as SSWAP and BioMoby, while allowing for more efficient semantic envelopment of computational chemistry services, resource discovery, and automated workflow organization. In this work, we apply the CHEMINF ontology and Chemical Entity Semantic Specification and demonstrate the usability of the SADI framework in solving common cheminformatics problems starting from RDF−based chemical entity representations. Our eventual goal is to convert all of the functions and functionalities of the Chemistry Development Kit (CDK) into distinct SADI services. This would enable the formulation of all cheminformatics problems currently addressed by CDK, as SPARQL queries, returning meaningful RDF output which can then be easily integrated with existing RDF−based knowledgebases or used for further processing.

The Open Source Predictive Toxicology Framework https://www.opentox.org, developed by partners of the EC FP7 OpenTox project , aims at providing a unified access to toxicity data and predictive models, as well as validation procedures. This is achieved by i) an information model, based on a common OWL−DL ontology https://www.opentox.org/api/1.1/opentox.owl ii) flexibility by linking with related ontologies; iii) availability of data and algorithms via a standardized REST web services interface, where every compound, data set or predictive method has an unique web address, used to retrieve its RDF representation, or initiate the calculations. The OpenTox framework allows building user−friendly applications for toxicological experts or model developers, or direct access by an application programming interface for development, integration and validation of new algorithms. The work presented describes the experience of building RESTful web services, based on RDF representation of resources, to incorporate diverse IT solutions into a distributed and interoperable system.

Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation. The work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC₅₀ and K_i values are modeled for a number of biological targets using data from the ChEMBL database. We have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.

Our Chemical e−Science Information Cloud (ChemCloud) − a Semantic Web based eScience infrastructure − integrates and automates a multitude of databases, tools and services in the domain of chemistry, pharmacy and bio−chemistry available at the Fachinformationszentrum Chemie (FIZ Chemie), at the Freie Universitaet Berlin (FUB), and on the public Web. Based on the approach of the W3C Linked Open Data initiative and the W3C Semantic Web technologies for ontologies and rules it semantically links and integrates knowledge from our W3C HCLS knowledge base hosted at the FUB, our multi−domain knowledge base DBpedia (Deutschland) implemented at FUB, which is extracted from Wikipedia (De) providing a public semantic resource for chemistry, and our well−established databases at FIZ Chemie such as ChemInform for organic reaction data, InfoTherm the leading source for thermophysical data, Chemisches Zentralblatt, the complete chemistry knowledge from 1830 to 1969, and ChemgaPedia the largest and most frequented e−Learning platform for Chemistry and related sciences in German language.

Resource Description Framework (RDF) and a set of associated technologies like OWL, SPARQL etc..., which form the W3C's semantic web technology stack, are renewing interest in semantic chemistry. Semantic Web Services not only specify syntactic interoperability but also specify and enforce the semantic constraints of messages being transmitted and objects being accessed. Liceptor database is a small molecule ligand database consisting of approximately 4 million compounds. The database schema consists of fields like molecular properties (2D−structure, molecular weight, molecular formula etc...), molecular descriptors (H−donors, H−acceptors, logP, logD number of rotational bonds etc...) and pharmacological properties (bio−assays, receptors, enzymes, parameters, animal models, therapeutic indications etc...). Pharmaceutical and Bio−Technology companies use this database to mine chemical space for internal research, to prioritize QSAR and pharmacophore studies, for synthetic chemistry endeavors and for advancing hit−to−lead patterns. The database records are available in multiple formats (relational database, XML, Rdfile etc...) as well as available online through an interactive web application (html format). The soon to be released version of the database includes access using semantic web services. The ontology is expressed in OWL and RDF defines the overall framework. Typical consumers of the data using this access mechanism are expected to be third−party tool vendors and data aggregators. Use of semantic web services allows evolution of the schema over time without explicitly communicating the change as well as requiring all data consumers to be changed.

This paper presents the oreChem Experiments Ontology, an extensible model that describes the formulation and enactment of scientific methods (referred to as “plans”), designed to enable new models of research and facilitate the dissemination of scientific data on the Semantic Web. Currently, a high level of domain−specific knowledge is required to identify and resolve the implicit links that exist between digital artefacts, constituting a significant barrier−to−entry for third parties that wish to discover and reuse published data. The oreChem ontology radically simplifies and clarifies the problem of representing an experiment to facilitate the discovery and re−use of the data in the correct context. We describe the main parts of the ontology and detail the enhancements made to the Southampton eCrystals repository to enable the publication of oreChem metadata.

In order to truly convert RDF−encoded chemical information into knowledge and break out of domain− and vendor−specific data silos, reliable chemical ontologies are necessary. To date, no standard ontology that addresses all chemical information representation and service integration needs has emerged from previously proposed ontologies, ironically threatening yet another “Tower of Babel” event in cheminformatics. To avoid resultant substantial ontology mapping costs, we hereby propose CHEMINF, a community−developed modular and unified ontology for chemical graphs, qualities, descriptors, algorithms, implementations, and data representations/formalisms. Further, CHEMINF is aligned with ontologies developed within the OBO Foundry effort, such as the Information Artifact Ontology. We present the application of CHEMINF to efficiently integrate two RDF−based chemical knowledgebases with different representation structures and aims, but common classes and properties from CHEMINF. Finally, we discuss the steps taken to ensure applicability of this ontology in the semantic envelopment of computational chemistry resources, algorithms, and their output.

Though the nature of RDF implies the ability to interoperate and integrate diverse knowledgebases, designing adequate and efficient RDF−based representations of knowledge concerning chemical entities is non−trivial. We hereby describe Chemical Entity Semantic Specification (CHESS), which captures chemical descriptors, molecular connectivity, functional composition, and geometric structure of chemical entities and their components. CHESS also handles multiple data sources and multiple conformers for molecules, as well as reactions and interactions. We demonstrate the generation of a chemical knowledgebase from disparate data sources, using which we conduct an analysis of the implications of design choices taken in CHESS on the efficiency of solutions for some classical cheminformatics problems, including molecular similarity searching and subgraph detection. We do this through automated conversion of SMILES−encoded query fragments into SPARQL queries and DL−Safe rules. Finally, we discuss approaches to identification of potential reaction participants and class members in chemical entity knowledgebases represented with CHESS.

Lipid nomenclature has yet to become a robust research tool for lipidomics or lipid research in general. This is in part because no rigorous structure based definitions exist for membership of specific lipid classes has existed. Recent work on the OWL−DL Lipid Ontology with defined axioms for class membership and has provided new opportunities to revisit the lipid nomenclature issue [1], [2]. Also necessary is a framework for sharing these axioms with scientists during scientific discourse and the drafting of publications. To achieve this we introduce here a new paradigm for Lipidomics researchers in which a client side application tags raw text about lipids with information, such as canonical name or relevant functional groups, derived from the ontology and is delivered using web services. Our approach includes following core components: (i)Semantic Assistant Framework [6]; (ii) Lipid ontology [4]; (iii) Ontological NLP methodology; (iv) Ontology Axiom−extractor for the GATE framework. The Semantic Assistant Framework is aservice−oriented architecture used to enhancing existing end−user clients, such Open Office Writter, with online Lipidomics text analysis capabilities provided as a set of web services. The Ontological NLP methodology links Lipid named entities occurred in a document opened on client side with existing ontologies on server side. The Ontology Axiom−extractor annotates each named entity with canonical name, class name and related class axioms providing annotation for documents on the client side. The proposed system is scalable and extensible allowing researchers to easily customize the information to be delivered as annotations depending on the availability of chemical ontologies with defined axioms linked to canonical names for chemical entities.

The primary method for scientific communication is in the form of published scientific articles and theses and the use of natural language combined with domain−specific terminology. As such, they contain unstructured data. Given the unquestionable usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high−throughput Natural Language Processing (NLP) approaches. Using chemical synthesis procedures as an exemplar, we present ChemicalTagger. ChemicalTagger is a tool that combines chemical entity recognisers such as OSCAR with tokenisers, part−of−speech taggers and shallow parsing tools to produce a formal structure of reactions. This extracted data can then be expressed in RDF. This allows for the generation of highly informative visualisations, such as visual document summaries, structured querying and further enrichment can be provided by linking with domain specific ontologies.

There is much interesting information about drugs that is available on the Web. Data sources range from medicinal chemistry results, to the impacts of drugs on gene expression, through to the results of drugs in clinical trials. Linking Open Drug Data (LODD) is a task within the W3C's Health Care Life Sciences Interest Group. LODD has surveyed publicly available data sets about drugs, created Linked Data representations of the data sets and interlinked them together, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task also actively explores best practices for exposing data in a Linked Data representation The figure below shows part of the data sets that have been published and interlinked by the task so far. The LODD data sets are represented in dark gray, while light gray represents other Linked Data from the life sciences, and white indicates data sets from different domains. Collectively, the LODD data sets consist of over 8 million RDF triples, which are interlinked by more than 370,000 RDF links. This presentation will introduce the LODD task and show examples of recent.

A suite of software was developed to control and monitor experimental and environmental data and used for probing of the air/water interface using Second Harmonic Generation. A centralised message broker enabled a common communication protocol between all objects in the system; experimental apparatus, data loggers, storage solutions and displays. The data and context are captured and represented in ways compatible with the Semantic Web. Experimental plans and the enactment are described using the oreChem experiments ontology; this provides the means to capture the metadata associated with the experimental process and the resulting data. Environmental data was stored in the Open Geospatial Consortium Sensor Observation Service (SOS). The SOS is part of the Sensor Web Enablement architecture; this describes a number of interoperable interfaces and metadata encodings for integrating sensors webs into the cloud. A mashup web interface was produced to link all these sources of information from a single point.

Chemical patents are a rich source of technical and scientific information. They include meta−data, such as bibliographic information, as well as scientific data relating to reactions and synthesis experiments. However, they are lengthy, largely unstructured and rich in technical terminology such that it takes a signification amount of human efforts for analyses. This would make them an ideal candidate for 'semantification'. As a demonstration, an RDF triplestore of chemical patents is created. The patents, provided by the European Patent Office, are in an XML format. Document segmentation is used initially to extract the relevant information, mainly bibliographic information and experimental paragraphs. The experimental paragraphs are then processed using Natural Language Processing tools to extract the various components of the chemical reaction; roles, such as reactant, product or solvent, are then assigned. This extracted information is then converted into RDF and stored in a triplestore where it can then be queried, visualised and basic inferences can be made.The ultimate goal of this semantic representation, is to make data available and re−usable by the scientific community.

We have recently developed a freely−available resource called Chem2Bio2RDF that consists of chemical, biological and chemogenomic datasets in a consistent RDF framework, along with SPARQL querying tools that have been extended to allow chemical structure and similarity searching. Chem2Bio2RDF allows integrated querying that crosses chemical and biological information including compounds, publications, drugs, genes, diseases, pathways and side−effects. It has been used for a variety of applications including investigation of compound polypharmacology, linking drug side−effects to pathways, and identifying potential multi−target pathway inhibitors. In the work reported here, we describe a new set of tools and methods that we have developed for querying and data mining in Chem2Bio2RDF, including: Linked Path Generation (a method for automatically identifying paths between datasets and generating SPARQL queries from these paths); an ontology for integrated chemical and biological information; a Cytoscape plugin that allows dynamic querying and network visualization of query results; and a facet−based browser for browsing results.

In common with most scientific disciplines, there has in the last few years been a huge increase in the amount of publicly−available and proprietary information pertinent to drug discovery, owing to a variety of factors including improvements in experimental technologies. So the big challenge for us is how we can use all of this information together in an intelligent way, in an integrative fashion. We are developing an application to mine relationships between Chemical and Gene/Disease/Pathway/Literature, and visualize them. It aims to help answer the question “anything else should I know about this compound?” from a medicinal chemistry perspective based on the full picture of chemicals. For the mining part, we have already developed an aggregating web services, named WENDI, which calls multiple individual or atomic, web services including diversity of compound−related data sources, predictive models and self−developed algorithms, and aggregates the results from these services in XML; For visualizing, two ways to go: First, we create a RDF reasoner to convert XML from WENDI to RDF, find inferred relationships based on RDF, rank evidences focused on chemical−disease, and print all evidences out by using SWP faceted browser based on Longwell https://simile.mit.edu/wiki/Longwell), it mixes the flexibility of the RDF data model with the faceted browser to enable users to browse complex RDF triples in a user−friendly and meaningful manner; Second, we place all relationships from WENDI into a chemical space consisted of 60M PubChem compounds, then clustered/highlighted particular chemical compounds with specific attributes, like gene/disease/pathway/literature by using PubChemBrowse, which is a customized visualization tool for cheminformatics research and provides a novel 3D data point browser that displays complex properties of massive data on commodity clients and supports fast interaction with an external property database via semantic web interface.