Discovery
AI has accelerated target generation. The bottleneck is now validation: are these targets expressed on the cells that matter, in the patients who need treatment? Our discovery capabilities start where most platforms stop, with single-cell resolution of clinically annotated human tissue.
Features
Find the biology worth pursuing. Grounded in real patients, not model assumptions.
Antibody-Based Target Discovery
Systematically identify surface antigens expressed on malignant cells, and critically, on tumor-initiating cells, using surface protein mapping and functional validation in primary tissue. Every candidate target is linked to longitudinal patient outcomes, so you know not just that a target is present, but whether it’s present on the cells that drive disease progression and relapse.
This is how we built the biological foundation for our AML ADC discovery program, and it’s the same capability available to pharma partners working in any of our active indications.
What your team gets: Ranked target candidates validated at single-cell resolution against real patient biology, with clinical outcome context attached.
Small Molecule Target Discovery
Identify and validate molecular targets for small molecule intervention through pathway profiling, transcriptomic signatures, and resistance mechanism analysis, all derived from primary patient tissue with linked clinical data. Our single-cell resolution reveals which molecular pathways are active in which cell populations, enabling target prioritization that reflects actual tumor biology rather than bulk averages.
What your team gets: Biologically differentiated targets with clear mechanistic rationale and patient-population context, ready for hit-finding campaigns.
Resistance Mechanism Profiling
Understand how tumors survive treatment, at single-cell resolution. We characterize the molecular mechanisms through which specific cell populations develop resistance to standard-of-care or investigational therapies, identifying the surviving subpopulations and the pathways they exploit. This informs rational combination strategies designed to address the biology of relapse, not just the biology of initial response.
What your team gets: A mechanistic map of resistance biology in your indication, with actionable combination hypotheses grounded in patient-level data.
The essential data layer for AI-driven medicine
Let’s build the evidence that moves your program forward.