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Once the knowledge graph is built on direction-graded evidence, your target selection has something to stand on.
The same knowledge graph that ranked candidates requalifies them at the validation stage. Contradicting evidence noted at ranking becomes a validation gate.
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Custom scoring across disease relevance, druggability, and proprietary signal. Delivered in production for COPD, metabolic disease, and complement biology programs.
Atlas supplies the pre-harmonized data that Cohorter queries. Cohort construction starts from clean, ontology-mapped records.
Polly Scout powers the data discovery phase of Elucidata's Data Partner service, and is the first step in every Program Partner engagement where evidence completeness determines shortlist quality.
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The primary program type. Elucidata processes 8M+ full-text papers and qualifies every gene-disease relationship as supporting, contradicting, or neutral before writing a KG edge. Every edge carries direction and confidence. Delivered in production for COPD, metabolic disease, and AML programs.
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The same qualified evidence layer used for target ranking requalifies relationships at the validation stage. Delivered in production for complement-mediated disease programs.
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Polly KG is available as an optional evidence layer in Gene Disease Target Assessment engagements, supplying the biomedical knowledge graph that Polly Lens scores against.
We integrate Scout into your stack in the model that fits your program. Elucidata handles the run; your team works with the results.
Web interface for KG exploration, target scoring dashboards, and evidence report generation via Polly Lens.
The KG MCP Server connects your AI tooling directly to Polly KG. List graphs, run Cypher, download results. In production as of Q2 2026.
We wire Polly KG into your stack via the Polly KG REST API, with Cypher and natural language queries supported and async execution for large result sets.
Guided AI research interface for querying the KG, exploring mechanisms, and generating evidence reports without writing queries. Also available via Slack integration.
Full API documentation and integration guides available on request.
Co-occurrence counts how often a gene and disease appear together. Polly's KG separates supporting from contradicting evidence before writing any edge. For target identification in drug discovery, that distinction determines which candidates make the shortlist.
Target Ranking narrows 20,000 genes to a ranked shortlist using the knowledge graph. Target Validation uses the same qualified evidence to stress-test specific candidates before wet-lab commit. Both use direction-graded evidence. The question changes: 'who should we pursue?' versus 'does this candidate hold up?'
The Polly Knowledge Graph gives your scoring layer direction-graded evidence, not raw co-occurrence counts. Candidates with mixed or contradicting evidence are flagged before they reach the shortlist.
Yes. Full-text processing recovers evidence from supplementary sections and methods text that abstract-only tools miss. Proprietary in-house datasets go through the same qualification pipeline as public literature.
PubMed Central literature is ingested continuously. Proprietary data integrates at program milestones.
The full range, from initial target discovery through target validation. The same KG that ranks 20,000 genes also carries the contradicting evidence your team needs at the validation stage.
AI models that query a co-occurrence graph amplify noise. Polly KG gives AI models qualified, direction-graded inputs, so your models start from defensible evidence.
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