Speed Drug Discovery with AI-Powered Search

The Hidden Cost of Scattered Scientific Data in Pharmaceutical R&D

Over 50% of the world’s leading pharmaceutical companies choose Sinequa.

Drug discovery timelines are long, expensive, and failure-prone. A significant portion of that cost is not science: it is the time researchers spend finding, reconciling, and re-creating information that already exists somewhere in the organization. Scattered data across isolated systems, outdated repositories, and disconnected databases means scientists duplicate analyses, miss prior findings, and make decisions with incomplete information.

The organizations that have solved this problem share a common infrastructure decision: they connected their scientific data through AI-powered cognitive search. The result is not just faster search. It is faster, better-informed science.

This whitepaper covers the business case for cognitive search in pharmaceutical R&D, the real cost of duplicated work in drug discovery, and how leading pharma organizations are using AI-powered search to compress timelines and improve decision quality across the research lifecycle.

What You Will Learn

• The real state of scientific data in modern pharmaceutical R&D and why fragmentation is a structural problem, not an organizational one
• How cognitive search transforms research: from time-consuming manual literature review and database queries to instant, unified access across all connected scientific sources
• The measurable cost of duplicated work in drug discovery and the mechanism by which AI-powered search eliminates it
• How the world’s leading pharma organizations are using cognitive search to accelerate decision-making from target identification through IND filing

Download the whitepaper.

Who This Whitepaper Is For

This resource is written for life sciences leaders responsible for R&D strategy, research productivity, and scientific knowledge infrastructure:

• Chief Scientific Officers and R&D Directors building the organizational case for AI-powered scientific knowledge access
• Heads of Research Informatics and Digital R&D evaluating cognitive search platforms for complex scientific data environments
• IT leaders in pharmaceutical organizations assessing the infrastructure requirements for connected, AI-powered research knowledge systems
• Innovation and Digital Transformation leaders in pharma who need a business-case framework for scientific search investment

If your organization is running multiple drug discovery programs and your researchers are spending significant time searching for information rather than generating new insights, this whitepaper addresses your context.

Why Drug Discovery Takes So Long, and Where AI Search Fits

The average time from target identification to approved drug is over ten years. The cost is measured in billions. These figures are well known across the industry, and enormous effort has gone into compressing them: high-throughput screening, computational chemistry, AI-driven target identification, adaptive trial designs. Each of these approaches addresses a specific bottleneck in the scientific or clinical process.

What receives less attention is the knowledge access bottleneck: the time that scientists, researchers, and decision-makers spend searching for information that already exists, failing to find it, and either making decisions without it or duplicating the work required to generate it.

This bottleneck is not visible in the same way that a failed Phase II trial is visible. It does not appear on a project timeline as “information search.” It appears as longer target validation cycles, more protocol iterations, more synthesis rounds, and more go/no-go decisions made with incomplete evidence. But it is real, it is measurable when organizations look for it, and it compounds across every program in the portfolio.

Cognitive search addresses this bottleneck directly. It is not a replacement for the scientific tools that run experiments, model compounds, or analyze clinical data. It is the access layer between those tools and the researchers who need to draw on their outputs. When that access layer works well, researchers spend more time on science and less time on search. When it does not work well, the cost is distributed invisibly across every stage of the research lifecycle.

What Is Cognitive Search, and How Does It Differ from Standard Enterprise Search?

Cognitive search is the application of AI, natural language processing, and machine learning to enterprise information retrieval. It differs from standard enterprise search in three fundamental ways that matter specifically in scientific and pharmaceutical R&D contexts.

• Understanding query intent, not just keywords. A pharmaceutical researcher searching for “kinase inhibitor selectivity in EGFR mutant NSCLC” is asking a conceptual question about a biological mechanism, a chemical class, a genetic context, and a clinical indication simultaneously. Standard keyword search requires the researcher to know which exact terms appear in source documents and to reformulate the query multiple times to capture related terminology. Cognitive search applies semantic understanding to the query, identifying the concepts involved and retrieving results that are relevant to the scientific question regardless of whether they use the exact search terms.
• Connecting across heterogeneous scientific sources. Research knowledge in pharmaceutical organizations lives in systems that were built independently and do not naturally share data: electronic lab notebooks, LIMS platforms, compound registration databases, internal report repositories, clinical data archives, and external scientific literature. Standard enterprise search tools index one or a few of these sources. Cognitive search connects across all of them, presenting unified results from a single query interface. A researcher asking about a compound’s metabolic stability profile can receive results drawn from internal assay data, prior study reports, regulatory filings, and published literature simultaneously.
• Extracting meaning from scientific content. Scientific documents are not plain text. They contain structured data tables, chemical structures, genomic annotations, statistical results, and domain-specific terminology that general-purpose search tools do not handle natively. Cognitive search applies NLP models trained on scientific and biomedical language to extract entities and relationships from scientific content, making it searchable by concept, compound class, target, indication, and mechanism, not just by keyword match.

The Cost of Duplicated Work in Drug Discovery

Duplicated research effort is one of the most consistent and least-discussed productivity drains in pharmaceutical R&D. It takes several forms, each with a distinct cost profile.

• Duplicated literature review. A researcher characterizing a new target will conduct a literature review to establish the state of knowledge about that target’s biology, existing tool compounds, prior clinical attempts, and known liabilities. In organizations without unified scientific search, this review is conducted by each researcher who works on the target, in each program, each time it becomes relevant. The work is not systematically captured and made findable. The next researcher starts over.
• Duplicated experimental work. When assay results, stability data, ADMET profiles, and formulation findings from prior programs are not findable, chemists and biologists run experiments that have already been run. The cost is not just the experiment itself: it is the time, the consumables, the capacity in shared experimental infrastructure, and the delay in the program timeline while results are awaited for questions that have already been answered.
• Duplicated competitive intelligence. Regulatory intelligence, patent landscape assessments, and competitor pipeline analyses are expensive to produce and highly time-sensitive. In organizations without systematic knowledge access, these analyses are regenerated repeatedly across programs and therapeutic areas rather than being built upon incrementally.
• Suboptimal decisions from incomplete evidence. The most expensive form of duplicated work is the decision made without the information that would have changed it: a go decision on a target that prior internal data would have flagged as problematic, an endpoint selection that historical response data would have refined, a patient population definition that prior trial analysis would have adjusted. These decisions are not reversed cheaply.

Cognitive search reduces all four of these costs by making prior work findable at the point of need. The mechanism is simple: if a researcher can find what has already been done, they can build on it rather than repeat it.

How Leading Pharma Organizations Use Cognitive Search

The adoption of cognitive search by more than half of the world’s leading pharmaceutical organizations reflects a platform that has been validated in the most demanding R&D environments. The use cases that drive the most measurable value fall into four categories.

• Accelerating target identification and validation. Early-stage researchers use cognitive search to synthesize information about potential targets from across internal experimental data, published literature, patent filings, and competitive intelligence in hours rather than weeks. The speed of this synthesis directly compresses the time from hypothesis to validated target.
• Supporting compound design and optimization. Medicinal chemistry teams use cognitive search to retrieve SAR data, ADMET profiles, and prior assay results for compound classes under investigation. By making prior experimental findings findable at the point of design decisions, cognitive search reduces the number of synthesis and assay cycles required to identify a viable lead candidate.
• Enabling knowledge reuse across therapeutic areas. Large pharmaceutical organizations have deep prior knowledge in specific therapeutic areas that is not systematically accessible to researchers working in adjacent areas. Cognitive search surfaces this cross-therapeutic knowledge, allowing teams to apply insights from prior programs to current challenges without requiring personal knowledge of what was done in a different program five years ago.
• Preparing regulatory submissions faster. Regulatory teams use cognitive search to locate prior agency correspondence, precedent language from earlier submissions, cross-study comparisons, and supporting data quickly, reducing the time required to prepare high-quality responses to agency questions and to build complete submission packages.