PerCuro-A Semantic Approach to Drug Discovery. Final Project Report submitted by Meenakshi Nagarajan Karthik Gomadam Hongyu Yang

Transcription

1 PerCuro-A Semantic Approach to Drug Discovery Final Project Report submitted by Meenakshi Nagarajan Karthik Gomadam Hongyu Yang Towards the fulfillment of the course Semantic Web CSCI 8350 Fall 2003 Under the guidance of Prof. Amit P. Sheth LSDIS Lab University of Georgia, Athens

2 PerCuro-A Semantic Approach to Drug Discovery Karthik Gomadam, Meenakshi Nagarajan, Hongyu Yang {karthik, nbmeena, hyy}uga.edu ABSTRACT Drug Discovery is a domain that presents an excellent case for the application of semantic techniques in Bioinformatics. The domain is one of the most ancient characterized by rich voluminous data. All the information in this domain serves as a basis for making decisions to advance and prioritize potential leads in the process of discovering a drug. Clearly, capture and integration of such information of a varied nature has been increasingly critical. The process of discovering a drug is therefore very complicated and requires investment of time, money and effort. The possible use of semantic techniques as a way to speed up the process of target identification has been our primary motivation for this project. Here we present a small prototype system, wherein we exploit the relationships between various domains in drug development while trying to answer questions significant to speeding the process of drug discovery. The software has been named PerCuro, which in Latin means to Cure. KEYWORDS Drug Discovery, Drug, Target, Pathogen, Chemical compound, molecules, functional group, Multi-Ontology, RDF, RQL INTRODUCTION The process of discovering a drug depends largely on the interactions of the receptor, pathogen and drug molecules. A disease is caused by a pathogen binding to a receptor. A drug used to cure a disease can either bind to a receptor or to a pathogen to yield suitable results. The process of a drug binding to a receptor is preferred, killing the pathogen at the site and preventing further influx of pathogens. This target chemical compound match is found based on several molecular and chemical properties of the two entities under question. This brings under consideration factors that determine how the two entities are potentially related. The use of semantic techniques to analyze such relationships is the crux of this project. Our approach towards speeding the drug discovery escalates to an effort towards multi-ontology integration. Implementation details discuss this in more detail. RELATED WORK Many companies have set out to build an integration platform providing ontologies and related tools focused on drug discovery. We will discuss the different approaches and compare it with the approach we have used in PerCuro. Most of the implementation

3 details of related approaches are not very clear because the product or idea is under development. BioWisdom BioWisdom s Discovery ontology products provide a comprehensive and detailed description of the life sciences domain. The Discovery Ontologies contain around 500,000 concepts describing targets, diseases, chemicals, anatomy, species, phenotypes, technologies, institutions and people. By use of an intuitive navigational facilities of an ontology browser, BioWisdom combines various indexing techniques and the database architecture of biomedical information systems. Sagitus Solutions Sagitus Solutions approach is towards exploiting the power of using different ontologies together. The OIL language that combines features from Frames and DLs and is designed to represent ontologies developed in different formalisms, allowing information transfer between them. By representing ontologies in OIL, Sagitus aims to facilitate data mining /management across data sources from genes to clinical trials. The vision is to develop and integrate new and existing ontologies to capture knowledge across the whole drug discovery process. 3 rd Millenium The claim is that meaningful understanding of biological pathways can be realized only through semantic integration of biological data within its biological concept. At 3 rd Millenium, a system that extracts high throughput biological data and places it in its biological context thereby enabling semantic integration and greater knowledge of biological pathways has been created. The purpose is to enable high throughput biology to measurable accelerate drug discovery. Netwrok Ineference Network Inference s Cerebra Server Platform combines with BioWisdom s comprehensive ontologies and deep domain expertise to enable discovery and innovation. They attempt to discover and show associations using a multi-relational ontology. Comparison with our work As opposed to Network Inference s use of one multi-relational ontology, our approach is to explore similar associations using multiple ontologies with potential multirelations in them. Our approach seems to be in line with that of Sagitus Solutions with the exception of use of OIL. Our toy ontologies are developed using Protégé and associations explored using RQL.

4 WHY ONTOLOGIES It is probably important to make a clear argument for why such an application would require the use of ontologies as opposed to continuing with traditional database approaches. The domain of drug discovery is inherently very complex more because of the multiple domains it encompasses. Ontologies provide for universal agreement of concepts within and across domains. Another reasoning is based on the fact that all the information being discovered is by exploring relationships between entities. Such an implementation would otherwise have been very complicated and expensive in terms of resources and time if implemented using database techniques. The idea is to show that this domain is composed of associations, some simple and some complex which can be best captured and traversed using ontologies and related techniques. INTER ONTOLOGY QUERYING The domain of drug discovery, as we have mentioned earlier is a very complex one comprising of several other complex domains. To name some of them Pharmacogenetics, Pharmacokinetics, molecular biology, genes and gene-function, immunology, macromolecule structure ontology, diseases, chemical compound, human anatomy etc. Creation and maintenance of a single ontology just for drug discovery would be very expensive. (any updates in the individual ontologies would mean updating the drug discovery ontology) This was our primary motivation of exploring associations using multiple ontologies instead of a single ontology. Given the diversity and complexity of biological information, it very unlikely that one single drug discovery ontology will be developed. However, individual ontologies are being developed and some of them are well on their way. PharmGKB, Tambis ontology, RiboWeb, EcoCvc, Molecular Biology Ontology, Gene Ontology, IMGT ontology, Star macromolecule structure ontology, GENAROM, EpoDB Controlled vocabulary are some of them. SOFTWARE ARCHITECTURE Our software architecture is diagrammatically represented below.

5 JSP Front End Control Class RQL Query Builder Inference Class RQL Query Engine Query language Ontologies Drug Disease Pathogen/ ASSUMPTIONS The aim of this project has been to show case what semantics can do for a domain like drug discovery. Towards this we have made certain assumptions that simplify the scope of our domain. With respect to ontology design, we have considered only pathological diseases. The chemical properties of molecules of drugs, receptors and pathogens that we have captured have been limited to those essential towards exploring certain associations. When considering multi ontology integration, it is necessary to consider the different formalisms in which the ontologies may have been developed. For the sake of simplicity, we have shown multi ontology integration of ontologies created using Protégé.

6 ONTOLOGY DESIGN A graph representation of our toy ontologies ( rdf and rdfs )have been shown below. SCENARIOS The following sections describe the four scenarios that we have used to show case certain associations in the domain of drug discovery that can be explored using semantic techniques. Query creation: Inter ontology querying is achieved by means of querying separate ontologies and making dynamic associations between concepts in the ontologies. The mapping of concepts is achieved by passing the output of one query (operating on one ontology) as the input to another query (operating on second ontology). Scenario1: Given a disease D; can we identify what functional groups that a drug trying to cure D must not contain? Description: A disease is caused by a pathogen binding to a receptor. A drug that has to cure the disease has to bind itself to the receptor site to stop further influx of pathogens and also kill the ones at the receptor site. This action hugely depends on the interactions of the drug and receptor molecules. The structural compatibility between two molecules is out of the scope of our data capture. However, the chemical properties of these molecules can be exploited to state whether they react favorable towards each other. If the receptor and drug molecules do not reach favorable, they produce toxins. A drug that ends up creating toxins a body in the process of curing a disease is not a good choice. Being able to make such eliminations at the stage of target identification reduces the number of chemicals that scientists have to consider in modeling a drug for that particular disease. The flow of information is shown in the picture below Look up the disease ontology to find the disease causing pathogen and receptor using the is_caused_by relationship Look up the pathogen and receptor ontology relation to get the func.grps for the specific receptor using the "has_func_group" Look up molecule class in drug ontology to find those functional groups that are toxic with the functional groups of the receptor. Drugs having those toxic functional groups cannot be used to cure the disease

7 Results:

8 Scenario2: Given a disease D; can we identify what functional groups that a drug trying to cure D can possibly contain? Description: A disease is caused by a pathogen binding to a receptor. A drug that has to cure the disease has to bind itself to the receptor site to stop further influx of pathogens and also kill the ones at the receptor site. This action hugely depends on the interactions of the drug and receptor molecules. The structural compatibility between two molecules is out of the scope of our data capture. However, the chemical properties of these molecules can be exploited to state whether they react favorably towards each other. If the receptor and drug molecules react favorably, they do not produce toxins. Structural compatibility is yet to be determined, but an elimination like this reduces the number of drugable compounds that a scientist has to consider in modeling a drug for that particular disease. The flow of information is shown in the picture below Look up at the disease ontology and get the receptor and the pathogen involved using the is caused by relation. Look up at the pathogen and receptor ontology using the "has_func_group" relation to get the func.grps for the specific receptor Select a function group and look up at drug-ontology using the "reacts_fav_with" relation to get the func.grps for the specific receptor Drugs having those favorable functional groups are potential cures for that disease. Results:

9

10 Scenario3: Given a specific disease D, whose cause in unknown, is there any existing drug that can suppress the disease symptoms? Description: This scenario explores any additional uses of an existing drug. When there is an outbreak of a new disease with undetermined causes, an existing drug that alleviates symptoms same as that of the new disease offers temporary relief to patients. Being able to answer such questions also explores new uses for a drug increasing its value Select a disease and find the symptoms of the Look up the drug ontology using the "has_indication" relation to get the conditions the drug is used to cure. Drugs used to alleviate the indications same as the symptoms for the disease are potential alleviators. Results:

11

12 Scenario4:Given a compound C, can it act as a drug for a particular disease D? Description: This scenario answers if a specific chemical compound can be used to cure a particular disease. The possible use of this scenario is to eliminate at this pass that this chemical compound cannot be used. An additional check of Lipinski s rule of five which are rules that test the drugability of a compound is also made in this scenario if the compound C is already Used as a drug for D Else, solve scenario 1 to check if C contains any of the toxic func groups. If so it cannot act as a drug for D If not, check if C obeys Lipinski s rules of drugability If step 3 returns yes, C can be used to cure D Results:

13 FUTURE WORK Although this project began as a means of applying semantics to drug discovery; the implementation forced us to look at it from an angle of multi-ontology integration with Drug Discovery as an example. Future work is to implement this idea using larger ontologies to also provide quantitative results on scalability. The inter-ontology querying is done by executing a sequence of RQL queries; the dynamically provide the mapping between concepts in the ontologies. The result of one query serves as the input for the second thus making an implicit mapping between the two entities that the input and output correspond to. Use of large scale ontologies will possibly mean a bigger result set and the cost of sequential querying to get to the desired result is therefore a bottleneck. Using some form of Logics and Rules to represent the mappings is one of our other considerations for future work. REFERENCES

14 ACKNOWLEDGEMENTS We would like to thank Prof. Philip Bowen, Chemistry Dept University of Georgia, Athens for sharing valuable time and ideas for the project. Prof Amit P.Sheth for his invaluable suggestions, continuous support and encouragement during the course of the project. The teaching assistants for the course - Kunal Verma, Cartic Ramakrishnan for all their time and suggestions and all students of the Semantic Web Course.