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AI-Powered Search & Analytics for Pharma: Takeda’s Journey to Become Information-Driven
Turning Enterprise Data Into Competitive Advantage in Pharma
According to Forrester Research, “relevant knowledge at the right time is priceless in the enterprise.” Nowhere is this more true than in the pharmaceutical industry, where the race to discover, develop, and commercialize new therapies depends entirely on how quickly researchers can access the right knowledge and act on it.
Pharma organizations generate and consume enormous volumes of data every day: trade databases, scientific publications, patents, clinical trial records, R&D repositories, and recorded patient interviews. Yet the vast majority of this knowledge remains fragmented across disconnected systems, inaccessible to the researchers, medical affairs teams, and business leaders who need it most. The result is duplicated work, missed scientific signals, and slower drug development timelines.
How Takeda Pharmaceutical Became Information-Driven
In this 40-minute on-demand webinar, Sean Liu, Global Head of Translational Science Systems at Takeda Pharmaceutical, shares how one of the world’s leading biopharmaceutical companies approached this challenge — and what it took to build an AI-powered knowledge infrastructure capable of connecting the dots across a global R&D organization.
Takeda’s journey offers a concrete, real-world blueprint for any pharma or life sciences organization looking to move beyond siloed search and toward a unified, intelligence-driven approach to scientific knowledge management.
What You’ll Learn
Jeff Evernham joins Sean to present the state of the art in enterprise Search & Analytics for life sciences — covering how leading organizations are leveraging this technology to:
- Stay ahead of the science — automatically alerting researchers to the latest developments in their therapeutic areas, across publications, preprints, and conference proceedings
- Identify the right partners — surfacing the most relevant scientific collaborators, CROs, and academic institutions based on research profile and publication history
- Map expert networks — finding internal and external Key Opinion Leaders (KOLs) and scientific experts on any given disease, target, or compound
- Detect emerging research trends — identifying early signals in scientific literature before they become mainstream
- Navigate chemical space — accessing drug and disease information starting directly from chemical structures, connecting compound data to clinical and scientific context
Why This Matters Now
The pharmaceutical industry is at an inflection point. With the rise of generative AI, large language models, and agentic systems, the ability to retrieve, synthesize, and act on scientific knowledge at scale is no longer a competitive differentiator — it is a baseline requirement. Organizations that have already invested in AI-powered search infrastructure, like Takeda, are positioned to compound that advantage as new AI capabilities emerge on top of a reliable, connected knowledge foundation.
Sinequa’s enterprise AI search platform connects to over 200 data sources — including internal repositories, public scientific databases, patent systems, and regulatory filings — applying deep natural language processing and semantic understanding to make all of an organization’s knowledge instantly accessible, regardless of format, language, or location.
Frequently Asked Questions
AI-powered search helps pharma teams instantly access relevant knowledge from thousands of internal and external sources — including scientific publications, clinical trial data, patents, and proprietary R&D records. By surfacing connections between compounds, targets, diseases, and researchers that would otherwise require manual review, it significantly reduces literature review time, eliminates duplicated research effort, and helps scientists identify promising directions faster.
Translational science bridges the gap between early-stage research and clinical development — turning laboratory discoveries into candidate therapies. It relies on synthesizing knowledge from diverse sources: genomics data, biomarker research, clinical outcomes, and competitive intelligence. AI-powered search makes this synthesis faster and more comprehensive by connecting all relevant data regardless of where it lives.
Takeda Pharmaceutical implemented Sinequa’s AI-powered search and analytics platform to unify access to scientific knowledge across its global R&D organization. The platform enables Takeda researchers to monitor scientific developments, identify collaborators, map expert networks, and navigate drug and disease information — all from a single intelligent search interface.
Scientific literature databases like PubMed or Embase index only publicly available publications. Enterprise AI search goes further — simultaneously querying internal data (proprietary research, clinical notes, lab data) alongside external sources, understanding natural language intent, and ranking results by relevance to the user’s specific context. It connects the public and private knowledge dimensions of R&D in a single interface.
Yes. AI-powered enterprise search platforms like Sinequa can analyze publication history, citation networks, conference participation, and clinical trial involvement to automatically surface and rank subject matter experts and KOLs on any given therapeutic area, disease, target, or compound — dramatically accelerating the work of medical affairs and business development teams.
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