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Life Sciences

Partnering with leaders in the life sciences to make knowledge more accessible and extract deep insights

Using Relational Knowledge Graphs

to solve key life sciences challenges

Genomics Data Accessibility

Challenge: Genomics Data Accessibility

Life sciences organizations have a great amount of genomics data stored in a multitude of legacy and newer cloud-based applications, due to the large amounts of storage needed for analysis. However, many times, these applications are not interconnected, making it difficult for researchers to find searchable and accessible genomics data across systems to use in understanding drug response markers. This can cause redundant experimentation and a decrease in efficiency for the creation of early stage drug development.

Solution: Semantic Search

RelationalAI leverages data as a product, via a data fabric. Semantic search enables researchers to dive into their data in a more natural way, accelerating discovery and investigations. Using the RelationalAI platform, data is continuously and automatically updated for complete visibility and context into the latest findings. Reduce the timeline from drug discovery and creation to production with operational improvements that can scale across enterprise teams.


Helps remove duplicate experimentation efforts, provides a unified view, and surfaces pivotal information immediately to researchers and scientists.

Protein Prediction Modeling

Challenge: Protein Prediction Modeling

Proteins are the building blocks of life; however they do not work independently in living organisms. They must bind to other ions and molecules to create specific interactions to achieve the protein’s functionality. These interactions occur many times in specific pocket-like areas of protein, called binding sites.

By identifying these binding sites accurately, scientists can use the information to understand drug-target interactions, pathogenesis of diseases, compound design, and so much more, which will effectively impact drug design in the laboratory.

Solution: Reasoning, Simulation, and ML

RelationalAI uses reasoning, simulation, and relational machine learning to map out 3D shapes of proteins, nucleic acids, and complex assemblies from Protein Data Bank and molecule interaction data from the archives of a life sciences organization. This can integrate with the visual identification of possible binding sites through a simulation, driving compound design well before any need for experimentation.


Reduces the need for experimentation on molecular interactions, which do not appear through simulation exercises.

Extracting Clinical Trial Data

Challenge: Extracting Clinical Trial Data

The number of scientific publications relating to clinical trials and subsequent research has grown exponentially over the past decade, with little to no signs of slowing down.

This amount of information relating to clinical trials makes it very difficult to keep up with recent literature, as most are not automatically archived. This leads to important information getting buried in text without being available for further analysis.

Solution: Knowledge Extraction and Querying

RelationalAI leverages AI/ML algorithms to extract key information from text using named-entity recognition (NER). The extracted information, together with domain knowledge, is then structured in a relational knowledge graph.

This allows clinicians to quickly find similar clinical trials underway and identify new information which can help with phase I and II trials, saving organizations time, money, and uncertainty as they work to bring a new drug to market.


Time spent querying domain-related information from clinical trial databases and finding new information is significantly reduced.

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