A Data Lakehouse for R&D

A Data Lakehouse for R&D:
Accelerating drug discovery with access to
“right data” at the “right time”
~80% faster information retrieval | 10X improvement in response contextualization
Demo Video

Challenge:

Scientists face significant challenges in understanding disease and drug targets due to the overwhelming volume of data they need to process, including patents, scientific publications, trial data, and internal documents. Extracting and summarizing this information is not only time-consuming and arduous but often results in incomplete or inaccurate insights.

Key questions include:

  • How can I quickly extract relevant information on diseases and drugs?
  • How can I gain actionable insights from historical experiment data?
  • How can I combine public domain data with proprietary datasets to uncover deeper connections?
  • How can I efficiently summarize knowledge from journals and internal documents?
  • Lastly, how can I achieve a longitudinal view of R&D to better inform decisions and strategy?
Addressing these challenges is critical for accelerating the pace of innovation and discovery in life sciences.

Solutions:

A data lakehouse built on tcgmcube comes powered with Gen-AI, semantic search, and knowledge graphs. The platform enables seamless analysis of unstructured data, such as text and images, to extract actionable knowledge. With Gen-AI-enabled intelligence, users can request and receive information in natural language, eliminating the need to write complex queries.

tcgmcube also incorporates a global knowledge graph developed over years of life sciences R&D expertise, delivering a deep understanding of medical context and intent. Information is structured within a robust ontology understood by scientists, the respective ontology and knowledge graph can be extended according to local client specific data enabling more contextual and intuitive responses.

The platform also supports multiple user interfaces for data dissemination, including Gen-AI, traditional AI, BI tools, knowledge graphs, and low-code front-ends, offering flexibility and adaptability.
A data lakehouse for R&D powered with a semantic layer, Gen-AI capabilities and Knowledge Graphs
Information requested and received in natural language
Provides contextual responses that is easy to use, understand & interpret
Information structured in an ontology that is understood by the scientist
Adherence to FAIR principles
Multiple user interfaces for
data dissemination

Proven Value Adds

80% faster information retrieval

10X improvement in response contextualization