Question

You are an ontology consultant and have to advise the clients on ontology development for the following scenario. What would your advice be, assuming there are sufficient resources to realize it? Consider topics such as language, reasoning services, bottom-up, top-down, methods/methodologies. * A pharmaceutical company is in the process of developing a drug to treat blood infections. There are about 100 candidate-chemicals in stock, categorised according to the BigPharmaChemicalsThesaurus, and they need to find out whether it meets their specification of the ‘ideal’ drug, codename DruTopiate, that has the required features to treat that disease (they already know that DruTopiate must have as part a benzene ring, must be water-soluble, smaller than 1 µm, etc). Instead of finding out by trial-and-error and test all 100 chemicals in the lab in costly experiments, they want to filter out candidate chemicals by automatic classification according to those DruTopiate features, and then experiment only with the few that match the desired properties. This in silico (on-the-computer) biomedical research is intended as a pilot study, and it is hoped that the successes obtained in related works, such as that of the protein phosphatases and ideal rubber molecules, can be achieved also in this case.

Short answer

Use OWL and a top-down approach to define DruTopiate's ideal properties. Utilize reasoning services to filter candidates and validate through a pilot study.

Step-by-step solution

Understand the Scenario

The pharmaceutical company is developing a drug called DruTopiate to treat blood infections. They need to filter 100 candidate chemicals by their properties to avoid costly lab experiments.

Identify Ontology Requirements

The ontology must include the properties of DruTopiate such as having a benzene ring, being water-soluble, and smaller than 1 µm. These properties will help automatically classify candidate chemicals.

Choose an Ontology Language

Recommend using a language like OWL (Web Ontology Language) because it supports rich representation of knowledge and reasoning services.

Reasoning Services

Advise the use of reasoning services to automatically classify candidate chemicals based on the defined properties of DruTopiate. This will help in filtering out the relevant chemicals efficiently.

Top-down Methodology

Recommend a top-down approach where the ideal properties of DruTopiate are defined first. These properties will serve as a framework to categorize and filter candidate chemicals.

Leverage Existing Thesaurus

Utilize the BigPharmaChemicalsThesaurus to categorize the 100 candidate chemicals. This existing resource can be integrated into the new ontology for consistency and efficiency.

Pilot Study Validation

Encourage validating the ontology and classification system through a pilot study. This can confirm the effectiveness of the ontology in identifying suitable candidate chemicals for lab testing.

Iterate Based on Results

Based on the pilot study results, iterate and refine the ontology. Make updates to improve classification and incorporate any new findings or requirements.

Key concepts

ontology language

An ontology language is crucial for defining the properties and relationships of concepts within an ontology. For ontology development in pharmaceuticals, using a robust and expressive language is essential. One of the most recommended ontology languages is OWL (Web Ontology Language). OWL allows for the representation of complex relationships between different chemical properties and provides support for reasoning services. This facilitates automatic classification and enhances interoperability among different systems. In this scenario, OWL can be employed to accurately represent the characteristics of candidate chemicals and the required properties of the ideal drug, DruTopiate.

reasoning services

Reasoning services play an integral role in ontology development. They enable the system to infer logical conclusions based on the defined ontology. In the context of pharmaceuticals, reasoning services can be used to automatically classify candidate chemicals according to their properties. Once the properties of DruTopiate, such as having a benzene ring, being water-soluble, and being smaller than 1 µm, are defined in the ontology, reasoning engines can filter out chemicals that meet these criteria. This significantly reduces the number of candidate chemicals to experiment with in the lab, saving time and resources.

top-down methodology

A top-down methodology is a strategic approach in ontology development where broader concepts are defined first, and then further refined into more specific categories. For this pharmaceutical scenario, the ideal properties of DruTopiate should be identified first. By establishing these high-level requirements, such as specific molecular features and solubility characteristics, the ontology can be structured effectively.
This approach ensures that the ontology aligns closely with the project’s goals and allows for efficient filtering of candidate chemicals. It serves as a guide for categorizing and analyzing the 100 chemicals in stock, ensuring that only those fitting the necessary criteria are selected for lab testing.

automatic classification

Automatic classification leverages the power of an ontology to categorize objects based on predefined criteria. In this pharmaceutical application, automatic classification is used to filter out candidate chemicals that possess the properties of the ideal drug DruTopiate. With properties like having a benzene ring, solubility in water, and a size smaller than 1 µm encoded in the ontology, reasoning services can quickly identify which candidate chemicals meet these requirements.
This process eliminates the need for extensive trial-and-error in the laboratory by narrowing down potential candidates to those that fit the desired profile, thereby saving both time and financial resources.

BigPharmaChemicalsThesaurus

The BigPharmaChemicalsThesaurus is an existing resource that categorizes chemicals commonly found in pharmaceuticals. Integrating this thesaurus into the new ontology will enhance the classification process by providing a structured and consistent reference for categorizing the 100 candidate chemicals.
Utilizing the thesaurus ensures that the ontology is built on a solid foundation of existing knowledge, facilitating interoperability and consistency in classification. This resource not only speeds up the ontology development process but also ensures the accuracy of the classifications, thereby aiding in the efficient identification of suitable candidate chemicals for the drug DruTopiate.