Build a complete drug pipeline landscape with Causaly competitive intelligence

For early research scientists and discovery teams in pharma, staying on top of activities across a therapeutic space is essential, because the decisions it informs—what targets to pursue, which assets to evaluate, and whether a program is sufficiently differentiated—carry significant financial effects.  

Pursuing the wrong target, missing a competitive signal, or overestimating differentiation can lead to years of wasted effort and substantial R&D spend. This extends beyond the programs they are actively developing to the drug discovery programs they are proposing, the external assets they are evaluating, and the competitive benchmarks shaping their scientific rationale.

The scientists making these decisions operate at the preclinical stage, where most pharmaceutical intelligence databases have limited coverage. These databases capture drug programs only when they have a formal paper trail, such as trial registrations, regulatory filings, or published studies. By Phase 2, most programs have produced enough documentation to be reliably covered.

In the discovery and preclinical stages, however, the signal is sparse and fleeting. A program may have minimal public information. Some never progress to additional patents, conference presentations, formal publications, or any clinical or regulatory documentation before they get discontinued. They may get absorbed into broader pipelines, folded into partnerships, or removed from the public domain as collaborations evolve. By the time a scientist goes looking for it, there’s no public trace.

Causaly Pipeline Graph processes those earlier sources continuously, giving R&D teams visibility into programs across the full development arc. The coverage data below shows how that approach plays out across three indicative oncology targets at different stages of development.

Why coverage gaps grow earlier in development

Pharmaceutical competitive intelligence data tends to flow through formal disclosure channels: regulatory filings, clinical trial registries, published literature, company press releases. Programs that have not yet reached those channels, particularly at the preclinical and early clinical stages, are absent from research documents.

Causaly ran a pharma pipeline benchmarking exercise across a representative sample of oncology targets spanning marketed, clinical, and preclinical development stages. A selection of those results is shown below.

The data illustrate a clear pattern. For targets with marketed drugs like DLL3, where years of regulatory filings and clinical trial reporting have created a deep public record, coverage is strong, and the proportion of programs unique to Pipeline Graph sits at 27%. At the clinical stage, with drugs like MICA, that proportion rises to 58%. At the preclinical stage, with drugs like PLXDC2, Pipeline Graph surfaces a third of the programs that do not appear in any registry-based sources.

Causaly's continuously monitor these sources and provide Pipeline Graph with its coverage depth at the earliest stages of development.

"I discovered a program for a target we are working on that was missing from our existing CI database, and our internal teams were unaware of it. This represented a partnership opportunity that saved us a lot of time instead of developing something ourselves."

— PRINCIPAL SCIENTIST, TOP 10 PHARMA

‍The challenge of reproducible drug discovery competitive intelligence

One reason the coverage problem persists is that most accessible alternatives to structured pharmaceutical databases — general-purpose AI tools and manual literature searches — do not solve it. They address different parts of the workflow. An LLM can summarize a paper or draft a landscape overview from training data, but it cannot monitor new data on an ongoing basis, track phase transitions as they occur, or surface an exhaustive list of programs across all stages of drug development.

Reproducibility is as important as coverage for decisions that need to be documented and revisited. Pipeline Graph returns the same programs for the same search each time, reflecting only genuine changes in the underlying data. That consistency is what makes it usable as a reference point for target prioritization decisions, go/no-go recommendations, and in-licensing evaluations.

Intelligence that covers the full R&D pipeline

Competitive intelligence for drug discovery looks different at every stage of R&D. Discovery teams working on target identification need different information from clinical development teams tracking trial execution, and both differ from BD&L teams evaluating in-licensing opportunities. Across all of them, the underlying need is the same: current, structured program data that covers the full development arc.

Pipeline Graph covers programs from the Discovery and Preclinical stages through to Launched drugs, filterable by modality, phase, status, target, indication, and more. It connects natively with Causaly's Agentic Research layer, so teams can move from a structured program overview directly into deeper comparative analysis: how Phase 3 programs compare on objective response rate, what safety signals are target-related, how a trial design differs from the field, without switching tools.

What scientists say  

The most meaningful test of any pharmaceutical competitive intelligence software is what happens when a scientist who knows a therapeutic area uses it. Across structured evaluations with scientists from large pharma and biotech teams, a few themes recur.

The themes that come up most often: finding the right programs takes longer than it should; there is no reliable way to stay updated as programs move through phases; and turning a data view into a shareable output for a portfolio review or decision meeting requires significant manual work.  

Pipeline Graph covers each of these directly, with structured views from Research to Launched, export to PowerPoint, and integrated agentic analysis for the deeper question-and-answer work that follows a landscape review.

Drug pipeline database coverage is uneven across development stages, and the programs that are hardest to find are often the ones worth finding earliest. Causaly Pipeline Graph gives R&D teams visibility into that earlier part of the development arc, so the picture they are working from reflects the full state of a space and not just the portion that has cleared formal disclosure channels.