The Artificial Intelligence (AI) industry has gone through major technological advances in providing optimized solutions to our complex modern problems. AI solutions have aided many industries in improving their production through automation and digitization. An example of such industries includes the healthcare industry that has benefited a lot from AI and machine learning.

Medicine production is a major segment of the healthcare industry that pharmaceutical and biopharmaceutical companies are responsible for. The biopharmaceutical industry differs from traditional pharmaceuticals for its production of drugs using living organisms instead of chemical compounds. 

Biopharma is a growing industry worth $401.32b in 2021 and is becoming increasingly popular for its predictive approach to bringing the most effective treatments to market. The market value of the biopharma industry is predicted to increase to nearly $1T by 2030. Like any large industry, Biopharma deals with analysis of vast amounts of data where data silos are a common threat. Healthcare outcomes cannot be improved without understanding the value of company data. AI can resolve this issue by enabling knowledge graphs to break departmental data silos.

Knowledge graphs can assist biopharma organizations in quickly analyzing their data to find relevant connections for getting better insights.

What is a Knowledge Graph?

A Knowledge Graph is a graphic representation of a network of data facts and the relationships between them. The data facts in knowledge graphs represent real-world entities such as people, places, objects, situations, etc. The information used to illustrate a knowledge graph is stored in a graph database in the form of entities and their relations.

In AI, knowledge graphs are used for turning data into machine-interpretable information that can deliver new facts about organizational knowledge. Knowledge graphs can contextualize both structured and unstructured data in the form of a network of meaningful connections. These graphs cater to real-world entities, and hence are specifically designed to handle the fluctuating nature of real world knowledge.

Elements of a Knowledge Graph

A knowledge graph comprises the following two elements:

• Entities: Entities are any real-world objects such as a person, place, event, etc. These are represented as nodes in a knowledge graph.
• Relationships: Relationships are any information that describes the connection between entities. They are represented as edges between nodes in a knowledge graph. Edges can be unidirectional or bidirectional, depending on the type of relationship. 

Key components of the Knowledge Graph

There are four key components involved in creating a knowledge graph:

• Taxonomy: Taxonomy refers to the vocabulary of a specific dataset for machines to learn. It is mostly in the form of categorization of entities in a way that is understandable by humans and machines both.
• Organizational ontology: Ontology is a formal representation of data entities based on taxonomies. The purpose of ontologies is to give structured meaning to data by formally defining relationships between entities.
• Content: Organizations gather their data from different sources such as databases and management systems. All of this data scattered across disparate sources is referred to as the content to be used in a knowledge graph.
• Graph database: A graph database is a comprehensive collection of all the organizational content. It stores references to content with their properties and relationships with other types of content. Knowledge graphs are ultimately illustrated using graph databases.

Knowledge Graph Data Sources in BioPharma

The biopharma industry works with many biomedical data types from multiple sources. The entities in biopharma knowledge graph can be drug labels, diseases, gene data, proteins, molecules, compounds, clinical trial data, and more. The relationships between these entities can be functional associations, drug interactions, molecular interactions, etc. All this information is backed by studies conducted by experts in the field.

Knowledge graphs can be used to handle all the different biopharma data types at once using machine learning algorithms. In biopharma, knowledge graphs can range from simple undirected graphs to powerful directed graphs depicting causal relationships. The directed graphs can be used to infer new information in the biopharma field.

The information used to create biopharma knowledge graphs can be gathered using multiple types of sources including:

• Manually curated databases where all previously known biopharma semantics are gathered in a graph database.
• Natural Language Processing (NLP) techniques to generate machine-understable information by analyzing natural language information about relationships between entities.
• Text mining techniques to extract relationship information from structured and unstructured content.

There are a variety of databases containing biomedical information that can be used as data sources for constructing knowledge graphs. Some well known biopharma data sources include:

• MEDLINE: A bibliographic database of biomedical and life sciences information. 
• PubChem: A database of chemical molecules and molecular functions.
• ClinicalTrials: A structured database of clinical studies conducted globally.
• UniProt: A collection of protein sequence and functional information.

Using AI and Knowledge Graphs for Drug Discovery

Drug discovery and production is crucial to healthcare in treating and preventing many illnesses. It can be optimized by recognizing the potential of AI and knowledge graphs for leveraging healthcare data to improve outcomes. WIth advances in AI, the focus is shifting from producing the same drug for a diverse population, to producing customized drug formulas based on a patient's gene data.

In drug discovery, knowledge graphs find their usage in multiple areas such as:

• Biomarker prediction: Using a biological predictive factor to make medical predictions. The predictive factor can be any medical attribute of an individual that can be used to infer outcomes related to a certain disease.
• Drug repurposing: Process of identifying new use cases for existing drugs to avoid the slow process of a new drug discovery.
• Target gene-disease prioritization: Prioritizing genes based on their probable association with a disease.
• Drug toxicity prediction: Computational identification of toxicity in a drug using data mining techniques.
• Clinical Trials: Research to evaluate health outcomes of new drugs.

Knowledge graphs integrate medical data in such a way that helps in quick retrieval of hidden insights. These graphs also help reduce errors and increase chances of success of a drug discovery in a cost-effective manner. For uncovering hidden correlations between medical data, analysts use different AI algorithms in drug discovery. Examples of such algorithms include tensor factorization, Deep Learning algorithms, and the different types of Artificial Neural Networks (ANNs). By visually exploring these correlations between medical entities, scientists can make timely decisions on sensitive treatment options.

The Future of BioPharma with Wisecube’s Knowledge Graph Engine

For success in the future, biopharma companies must optimize their operational capabilities to keep with the exponentially growing biomedical data. The biopharma industry holds great potential for transforming the healthcare sector and affecting the lives of millions of people.

Artificial intelligence forms the basis of quick discovery of correlations between bulks of data. For biopharma companies who wish to optimize their drug discovery processes, Wisecube’s AI-enabled Knowledge Graph Engine is a step in the right direction.

The Wisecube Knowledge Graph Engine can help unify and synthesize hidden patterns in your biomedical data. It can also learn from these patterns and make intelligent predictions based on it. By uncovering insights quickly, you can stay on top of the latest biomedical research and unlock the next level of drug discoveries.

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