Drug program insights for pharma and biotech R&D for pharma and biotech R&D
Answer the questions your drug program has
Your scientists already hold the questions a drug program needs answered – is expression high enough in tumor, will it spare healthy tissue, is it on the cell surface, does the protein shed, is it spatially colocalized, what’s in the safety reports, what the competitive landscape looks like. What they don’t always have is the way to answer them at the depth and speed the program demands.
Ardigen builds a research environment where your scientists explore a full evidence dossier, tailored to your drug modality, data and indication.
The scientific question is clear. The data is not ready to answer it.
Your scientists already hold the questions: is the target expressed highly enough in tumor, will it spare healthy tissue, is it on the cell surface, does it shed, what do the safety reports say, what is the competitive landscape. What they lack is a fast, deep way to answer them.
Choosing the right sources, normalization and comparators, and knowing which to trust when they disagree, is expertise in itself. Generic tools decide once, for everyone; every new question means a new plot, and answers land as spreadsheets nobody browses.
Where the work gets stuck
Confidence
Decisions get made on a limited panel of evidence.
Precision
Decisions rest on generic analyses, not fitted to the program or drug modality.
Throughput
Teams evaluate only what their capacity allows. Most of the space is never looked at, while experiments wait and competitors crowd the area.
Reach
The answer sits in one person’s notebook, and the rest of the organisation can’t use it.
Scientists bring the biological questions a drug program already has: drug-modality fitting above all: does this target hold up for the format you actually build, an ADC, a bispecific, a cell therapy.
We compose bespoke solutions from proven modules, fitted to the drug modality you build. Your scientists get one AI research environment to interrogate many lines of evidence side by side.
We also build what sits underneath, the data products and the knowledge-graph operationalization the answers run on. Most vendors consume a foundation; we build one.
How it works
Start with the question
Scientists bring the biological question, phrased as biology: expression, selectivity, surface presence, shedding, spatial co-location, safety, drug-modality fit.
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Data selection and mining
We select the right data products, or build custom ones, and run analytical modules, including orthogonal approaches, chosen for your drug modality.
Get back lines of evidence
A browsable AI-based dashboard, with every value traceable to source. A new question does not need a new project.
Built for the questions you already ask
Target expression and selectivity
Is expression high enough in tumor, will it spare healthy tissue
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Surface and shedding
Is it on the cell surface, does the protein shed
Spatial context
Is it spatially colocalized
Therapeutic window and safety
What’s in the safety reports, adverse-effect evidence
Drug-modality fitting
ADC, bispecific and cell therapy each need different things true of the same target.
Landscape
Patient segmentation, indication selection, competitive positioning
Benefits
Answer sooner
Cover demand without an internal build project.
Broaden the hypothesis set
Beyond what any one person had time to reach.
Keep it browsable
The next question doesn’t restart the work.
Survive challenge
Every number links to where it came from.
Make it usable org-wide
The answer leaves one person’s notebook.
Turn the questions you can't reach into evidence you can defend
We are the data and AI experts who speak your drug-discovery language.
Drug-modality fitting
An ADC, a bispecific and a cell therapy need different things to be true of the same target. We answer for the format you actually build.
Data experts who speak biology
Normalisation, cut-offs, source weighting and what to do when they disagree. Selecting the target stays your science.
We build what sits underneath
The data products and knowledge-graph work the answers run on. Product vendors ship the front end and assume the rest.
You can check every number
Each plot and answer links to the data it came from.
We get the comparison right
We go deep enough into your program to know whether a given expression means high or low, and we show the reasoning.
Consumable, not just processed
Products your scientists and agents query directly, built to GxP and HIPAA.
Confidence from convergence
Depth across modalities lets us answer the same question from independent directions.
A system, not a report
Composed per program, and it keeps working: agents watch the sources and new data re-triggers what depends on it.
Frequently Asked Questions
What are drug program insights?
Data-sourced answers to the biological questions a drug program is deciding on, target, biomarker, modality-fit, safety and competitive landscape, delivered as lines of evidence your scientists can browse, interrogate and trace to source, rather than a static report.
How do you evaluate whether a target fits an ADC, bispecific or cell therapy?
Each format needs different things true of the same target. We assemble and weight the evidence for the modality you actually build, expression, selectivity, surface presence, shedding and safety, not a generic ranked list.
What data do you use to answer drug-program questions?
Internal, public and third-party data across 10+ modalities, with bespoke normalisation and source weighting, including the judgement of what to do when sources disagree.
How is this different from a static report or an off-the-shelf target-scoring tool?
A static report is frozen on delivery; a scoring tool decides once, for everyone, and returns a single score. We deliver a full evidence dossier inside a research environment that stays browsable, is fitted to your program, a re-runs as new data arrives.
Can you combine our proprietary data with public sources?
Yes. We integrate proprietary evidence with the public and third-party context needed to interpret it, and build the data products underneath if you need them.
How long does it take to get answers?
A first proof of concept typically lands in 2–4 weeks, with a full program engagement over 2–6 months.
Does the data stay in our environment, and is it compliant?
Yes, deployable in your environment and built to GxP and HIPAA. Your scientists and AI agents query it directly through the tools you already use.
Do you choose our target for us?
No. We assemble and weight the evidence; your scientists keep the decision.
Start with one scientific question, modality or data bottleneck
See the impact
Case studies
Contact
Bring us the question your program is stuck on
Tell us the decision you’re making and the format you’re building for. We’ll show you the lines of evidence behind it.
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