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Unlocking the Wealth of R&D Data
Why R&D Data Remains Locked in Life Sciences
Pharmaceutical and biotech organizations generate enormous volumes of data every day — from clinical trial results and patent filings to EHR records, scientific publications, and proprietary lab notes. Yet most of this knowledge remains siloed across disconnected systems, inaccessible to the researchers and scientists who need it most.
According to Forrester Research, knowledge workers spend up to 19% of their time searching for information. In an industry where a single insight can accelerate a drug candidate by months — or eliminate a dead-end early — that time has a direct cost measured in millions.
What You’ll Learn in This Webinar
In this session, Sinequa and SciBite demonstrate how combining enterprise AI search with life sciences-specific semantic enrichment transforms how pharma teams navigate their R&D data landscape. Specifically, you will see:
- How cognitive search surfaces relevant knowledge from both structured and unstructured data sources — including clinical reports, scientific literature, and internal repositories
- Why biomedical ontologies (MeSH, GO, ChEBI, and more) dramatically improve search precision by understanding entities, synonyms, and biological relationships
- How the Sinequa + SciBite integration enables smarter, context-aware search across patent databases, regulatory documents, and internal knowledge bases
- Real use cases from leading pharma organizations that have reduced literature review time, improved regulatory compliance research, and accelerated competitive intelligence
About the Speakers
- Lee Harland — Founder & Chief Scientific Officer, SciBite Lee is a pioneer in applying computational biology and semantic technologies to drug discovery. At SciBite, he leads the development of TERMite and CENtree, industry-leading biomedical ontology tools used by top-tier pharma and biotech firms globally.
- Gengis Birsen — Senior Sales Engineer, Sinequa Gengis specializes in deploying enterprise AI search solutions for complex, data-intensive industries. He has worked with leading life sciences organizations to architect knowledge management systems that connect disparate R&D data sources into unified, searchable intelligence.
Who Should Watch
This webinar is designed for professionals in life sciences, pharma, and biotech who are responsible for research operations, knowledge management, competitive intelligence, or digital transformation including:
- R&D Directors and VP of Research
- Knowledge Management and Information Science leads
- Digital and Data Science teams in pharma
- IT leaders evaluating enterprise search or AI platforms
Watch the video
Frequently Asked Questions
R&D data in life sciences refers to all structured and unstructured information generated throughout the drug discovery and development process — including clinical trial results, scientific publications, patent filings, compound databases, lab notebooks, genomics data, and regulatory submissions.
AI-powered enterprise search helps pharma teams find relevant knowledge faster by understanding the meaning of scientific queries — not just keywords. It connects data across disconnected systems, applies biomedical ontologies to normalize terminology, and surfaces insights from millions of documents in seconds.
A biomedical ontology is a structured vocabulary that defines biological entities — genes, diseases, compounds, pathways — and their relationships. When applied to search, ontologies allow systems to understand that different terms refer to the same concept, dramatically improving recall and precision for scientific queries.
Sinequa’s enterprise AI search platform integrates with SciBite’s semantic enrichment tools to deliver ontology-powered search across life sciences data. SciBite’s TERMite technology tags biological entities within documents at indexing time, enabling Sinequa to return biologically-aware search results.
This webinar is aimed at R&D leaders, knowledge management professionals, data science teams, and IT decision-makers in pharmaceutical, biotech, and life sciences organizations who are exploring how to improve access to scientific knowledge through AI and intelligent search.
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