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Teams need to evaluate 10–20 competing targets simultaneously across modalities, with no reliable framework.
You bring your differentiated therapeutic thesis and proprietary data. Elucidata co-builds a program-specific target decision system that connects evidence & surfaces targets your team can act on.
We harmonize your proprietary omics, assay, and experimental data, connect it to curated evidence from 20+ public sources, and run quality checks before it enters the graph, turning 20,000 genes into an evidence-linked target landscape.
The knowledge graph is built around your program context and not just a broad disease label. It connects evidence across genetics, transcriptomics, proteomics, and disease mechanisms for every target, and helps filter generic associations into program-relevant targets.
Each target is ranked using a custom scoring framework aligned to your differentiated therapeutic thesis. Our in-silico perturbation model stress-tests hits across unscreened, disease-relevant contexts to show which signals are likely to hold up.
Your team gets a Target Dashboard with evidence grades for each target across genetics, multi-modal omics, disease biology, druggability, and confidence level. Every ranked candidate includes mechanistic rationale and source-linked evidence, helping your team decide which 2 targets are worth investing in.
Polly ingests your proprietary data and public biomedical evidence, connects it through a knowledge graph, scores candidates across 14 dimensions, and delivers a ranked, decision-ready target list your team can defend.
LLMs summarize papers. Generic AI Tools surface associations. We build knowledge graphs & models that show the mechanistic trail behind every target recommendation.

A Massachusetts-based therapeutics company sought to accelerate AML target-indication assessment using differentiation therapy, a novel approach that transforms malignant cells into healthy functional ones.

A Boston-based biotech focused on immune and metabolic diseases was hampered by siloed non-model datasets; our scalable ETL pipeline and Base-KG unified them into one AI-ready foundation from day one.

In collaboration with Elucidata, a US-based therapeutics company identified a novel Acute Myeloid Leukemia target in just 6 months. It has advanced to clinical trials, offering hope to 100k+ patients.

A pharmaceutical company based in Boston aimed to speed up their target discovery and validation process for inflammatory disease using single-cell RNA-seq data.