AI Target Discovery
Rank the targets that belong on your bench
Assaygrove reads your omics and the literature the way a computational biologist would, then tells you which ten targets are worth a wet-lab program.
The Cost of Getting It Wrong
Figures from published biotech failure analyses and industry post-mortem reviews
Target selection is where most programs fail silently
In published post-mortems of failed drug programs, the root cause is rarely chemistry or formulation. It is a target that was prioritized before the evidence was properly integrated. A target that looked compelling in one dataset and was never cross-referenced against the full omics picture.
The problem is not that research teams lack the skills. It is that the process of integrating omics data, literature signals, and genetic evidence into a coherent ranked list takes months of focused computational biology work. Most teams do not have the bandwidth to do that rigorously for every candidate before committing bench time.
How It Works
Three steps to a defensible target list
Connect
Upload or connect omics datasets: bulk RNA-seq expression matrices, proteomics data, GWAS summary statistics. Standard file formats. No PHI required.
Rank
Multi-evidence scoring integrates your omics signals against 40M+ indexed literature records, genetic associations, and functional evidence. Each target receives a composite confidence score with a decomposable breakdown.
Decide
Receive a ranked target list with per-target evidence cards: genetic evidence tier, functional support, literature confidence, and a recommended priority action. Export to CSV or JSON.
Early Access
What research teams are seeing
"We ran our oncology target shortlist through Assaygrove during our early-access pilot. The ranking surfaced a target we had deprioritized based on a single dataset. The multi-omics view changed our decision. That program is now in active validation."
"The evidence card decomposition is what sold me. Not a black-box score, but three separate evidence components I could interrogate. As a VP Research, I need to defend prioritization decisions to the board. This gave me the language to do that."
Early-access research teams reprioritized away from their initial lead target based on Assaygrove ranking
From our early-access program, Q3 2025
What You Actually See
A full evidence breakdown for every target
Each ranked target comes with a decomposable evidence card. Not a single confidence number. Three scored components: genetic evidence (GWAS hits, eQTLs, rare variant burden), functional evidence (knockout phenotypes, protein interactions), and literature evidence (publication density, recency weighting, entity confidence).
Recommended priority action tells you exactly what the evidence supports: Prioritize, Monitor, or Deprioritize, with the specific tier driving the call.
Strong GWAS association in colorectal carcinoma (p = 3.2e-12). eQTL signal in colon tissue. Rare variant burden in familial CRC cohort.
- CRISPR knockout reduces proliferation in 3 cell lines
- 17 high-confidence protein interaction partners
Commit bench time to the targets that deserve it
A rigorous ranked target list, built the way a computational biologist builds it, available in hours rather than months.
Boston-based team, founded 2024. Independently funded.