Inform Online 2020 – Unlocking the Wealth of R&D Data for Life Sciences (Scibite)

Why Scientific R&D Data Requires More Than Standard Enterprise Search

Life sciences R&D data presents a knowledge retrieval challenge that standard enterprise search platforms were not designed to solve. The problem is not just volume — it is semantic richness. Scientific text encodes meaning through a specialized vocabulary of compound names, gene targets, protein families, disease indications, clinical endpoints, and regulatory terminology that exists in multiple synonymous forms, nested ontological relationships, and cross-referencing patterns that keyword-based retrieval cannot reliably navigate. A search for a compound returns results about the compound but misses the equally relevant results about its structural analogues, related mechanisms, or the clinical trial that studied it under a different name.

The joint platform this session demonstrates combines both layers: Scibite’s ontological enrichment applied at the data ingestion layer, feeding into Sinequa’s enterprise search and retrieval — creating an R&D intelligence platform that understands scientific meaning and makes it accessible across the full enterprise knowledge environment.

What the Session Demonstrates

• The R&D Data Problem: Why Scientific Knowledge Remains Locked: The session opens with a precise diagnosis of why life sciences R&D data remains practically inaccessible despite organizations having invested significantly in data collection and storage. The core issue is the gap between data that exists and data that is findable: internal research data, scientific literature, clinical trial records, and regulatory filings all exist within the organization’s systems, but the semantic complexity of scientific language means that keyword search misses the majority of relevant content for any given research query. The session quantifies the cognitive burden this places on knowledge workers — the manual effort required to navigate this fragmented, incompletely searchable data environment — and establishes why the solution requires both ontological enrichment and enterprise search, not either alone.
• Scibite’s Technology: Biomedical Ontology and Entity Recognition: The session demonstrates Scibite’s core capabilities in the context of life sciences R&D search: TERMite, Scibite’s biomedical entity recognition engine, which identifies and normalizes scientific entities (compounds, genes, proteins, diseases, clinical endpoints, mechanisms of action) against curated ontologies including ChEMBL, UniProt, MeSH, and proprietary biomedical ontologies; and CENtree, Scibite’s ontology management platform, which maintains the semantic relationships between entities that enable downstream search to retrieve results based on ontological relationships, not just literal text matches. The session explains how this enrichment layer operates at document ingestion, tagging content with normalized entity identifiers that make it semantically searchable before it reaches the search index.
• The Sinequa + Scibite Integration: How the Joint Platform Works: The session demonstrates the integrated architecture: Scibite’s entity enrichment is applied to incoming scientific content before it enters Sinequa’s search index, adding a layer of semantic metadata that the Sinequa retrieval layer can exploit for faceted search, semantic relevance ranking, and entity-aware query expansion. The practical result is demonstrated through live queries: a research question about a specific compound pathway retrieves the full semantically relevant evidence base across internal and external sources — including documents that describe the mechanism, the related compounds, the relevant clinical findings, and the regulatory precedents, regardless of the specific terminology used in each source document.
• Use Cases: Where the Joint Platform Delivers the Most Impact: The session covers the specific R&D workflows where the Scibite + Sinequa combination delivers the highest value: drug target identification and validation (finding all relevant evidence about a target across the full literature and internal research record), compound screening support (surfacing prior research on structural analogues and related compounds from internal screening data and published literature simultaneously), pharmacovigilance signal detection (identifying safety-relevant patterns across adverse event data, clinical records, and published case reports using normalized medical terminology), and competitive intelligence (tracking competitor pipeline and publication activity organized by compound class, indication, and mechanism using ontology-based categorization rather than keyword monitoring).