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Inform Online 2020 – Obtaining Relevant Insights Leveraging Unstructured Data in the Digital Workplace (Atos)
AI-Powered Search for Pharmaceutical R&D
How Unstructured Data Intelligence Accelerates Drug Discovery and Reduces Time to Market
Pharmaceutical R&D is one of the most knowledge-intensive processes in any industry. The data that determines whether a drug candidate advances or fails, clinical trial records, scientific literature, regulatory submissions, safety reports, compound databases, genomic data, and the institutional expertise of researchers who have worked on related programs, exists across dozens of disconnected systems that were never designed to be searched together.
The result is a knowledge management problem that directly affects competitive outcomes: research teams repeating work that has already been done elsewhere in the organization, drug discovery workflows that miss relevant prior findings because the scientist who produced them is in a different business unit, and clinical development processes slowed by the inability to rapidly synthesize evidence across the full data environment
.In this practitioner session recorded at Inform Online 2020, Frank Grognet from Atos, a global technology services leader and Sinequa implementation partner, shares how he worked with pharmaceutical organizations to address exactly these challenges: deploying Sinequa’s intelligent search platform to make unstructured R&D data accessible, synthesizable, and actionable across the full knowledge environment of a large pharma organization.
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What the Session Covers
The Unstructured Data Challenge in Pharmaceutical R&D
The majority of valuable knowledge in a pharmaceutical organization lives in unstructured form: scientific papers, clinical study reports, regulatory correspondence, lab notebooks, meeting records, and the outputs of decades of research programs. Traditional search tools can index structured databases. They cannot synthesize meaning from unstructured scientific text, surface the relevant expertise hidden in a researcher’s historical publication record, or connect a current compound development question to a related finding from a different therapeutic area three years ago. Frank Grognet describes the specific knowledge fragmentation patterns that characterize large pharma R&D environments and the operational consequences they create.
Improving Drug Discovery Through Intelligent Search
Drug discovery depends on researchers being able to rapidly assess what is already known about a compound, a target, a mechanism, or a safety signal, drawing on internal research, scientific literature, regulatory databases, and competitive intelligence simultaneously. The session demonstrates how Sinequa’s intelligent search capabilities, NLP-powered content analysis, semantic entity recognition across scientific terminology, and unified retrieval across disconnected data sources, enable research teams to conduct the comprehensive evidence synthesis that drug discovery requires, without the manual aggregation overhead that slows discovery timelines.
Surfacing Insights for Complex Clinical Trials
Clinical trial design and execution generates and requires continuous access to vast volumes of evidence: protocol design precedents, adverse event signals, patient population data, regulatory guidance, and competitive trial landscapes. The session covers how intelligent search applied to clinical data environments accelerates the evidence synthesis that clinical teams need, surfacing the relevant findings across internal and external sources that inform trial design decisions and safety monitoring.
Expert Discovery: Finding Who Knows What Across the Organization
One of the most underappreciated knowledge management challenges in large pharma organizations is expert discovery: when a research question arises in one team, the expertise to answer it may sit in a completely different part of the organization in a researcher’s publication history, in a project record from a discontinued program, or in a regulatory submission authored by a colleague in a different business unit. The session demonstrates how Sinequa’s entity recognition and expertise profiling capabilities surface the implicit expertise hidden in document records, connecting current research needs to the right internal knowledge owners.
Reducing Time to Market Through Knowledge Accessibility
The compounding effect of knowledge management improvements in pharmaceutical R&D is measurable in competitive terms: faster discovery timelines, more efficient clinical development, and reduced duplication of research effort translate directly into earlier regulatory submissions and earlier market access for therapies that improve patient outcomes. The session connects the knowledge management investments to their downstream competitive and commercial impact.
Frequently Asked Question
Unstructured data — scientific literature, clinical study reports, regulatory submissions, lab records, safety reports, research notes, and the outputs of decades of discovery programs — represents the majority of valuable knowledge in a pharmaceutical organization, and the majority of what traditional data management tools cannot access effectively. Structured databases hold well-defined fields that are straightforward to query; unstructured documents hold the meaning, context, and nuanced scientific judgment that determines how findings should be interpreted and applied. The difficulty is not that this data does not exist — pharmaceutical organizations typically have extraordinarily rich unstructured data environments — it is that making it findable, synthesizable, and searchable across the full organizational data environment requires NLP-powered content analysis and semantic retrieval capabilities that standard enterprise search tools do not provide. The result, without intelligent search, is a knowledge environment where researchers can only access what they specifically know to look for, in the systems they specifically know to search, missing the broader context that might change the direction of a research program.
Drug discovery accelerates when researchers can rapidly and comprehensively assess what is already known — internally and externally — about a compound, target, mechanism, or safety question, without spending the majority of their time on manual evidence aggregation. Intelligent search enables this by connecting across the full data environment of a pharmaceutical organization simultaneously: internal research databases, scientific literature repositories, regulatory submissions, clinical records, and competitive intelligence sources, all searchable through a single query with results ranked by semantic relevance to the specific research context. The impact is measurable at multiple stages of the discovery process: researchers find relevant prior work from other internal programs that changes how they approach a current problem, safety teams surface signals from historical data that inform compound development decisions, and clinical teams access precedent evidence for trial design decisions without manual literature review cycles that take weeks.
Expert discovery is the ability to identify, within a large organization, which individuals have specific knowledge or experience relevant to a current research or development question. In pharmaceutical organizations with thousands of researchers across global sites and therapeutic areas, the expertise relevant to any given question may sit in a researcher’s publication and patent record, a project history from a discontinued program, a regulatory filing authored five years ago, or the documented findings of a team working in a parallel therapeutic area. Without intelligent search, this expertise is inaccessible — researchers ask colleagues they already know, miss the experts they do not know to ask, and duplicate work that has already been done. Sinequa’s entity recognition and expertise profiling capabilities analyze the full text record of an organization’s documents to build implicit expertise profiles — identifying who has written about, contributed to, or demonstrated knowledge of specific scientific topics — making expert discovery a systematic capability rather than a network-dependent one.
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