score-target

name	score-target
description	Deep-dive validation of a single gene target across one or more diseases. Use when the user wants detailed scoring, a deep-dive, SWOT analysis, or comprehensive profile of a single drug target. Triggers on "score target", "deep dive", "profile target", "analyze target".

Deep-Dive Target Scoring

Perform an in-depth validation of a single target across one or more diseases.

Usage

/score-target PCSK9 — cardiovascular disease, familial hypercholesterolemia

Instructions

Given $ARGUMENTS:

1. Parse. Extract one target and one or more diseases from the input.

2. Resolve. Use the open-targets-platform MCP tool search_entities to map to canonical Ensembl Gene IDs and EFO Disease IDs.

3. Deep data collection. Go beyond what open_targets_client.py fetches — use the MCP tools get_open_targets_graphql_schema/get_type_dependencies to find the right fields, then query_open_targets_graphql (or batch_query_open_targets_graphql for multiple diseases at once) to also fetch:

   • Genetic constraint (pLI, LOEUF from gnomAD via Open Targets)
   • Mouse phenotypes (IMPC data from Open Targets)
   • Tissue expression (baseline expression from Open Targets)
   • Pharmacogenomics data if available
   • Safety liabilities and adverse events

4. Supplementary search. Use web search to find:

   • Latest clinical trial updates on ClinicalTrials.gov
   • Recent publications (last 2 years) about this target
   • Patent landscape (any composition-of-matter patents?)
   • Competitive landscape (who else is developing drugs against this target?)

5. Score. Apply the standard rubrics, but also provide sub-scores:

   • Clinical: Genetic evidence sub-score + Trial evidence sub-score
   • Druggability: Structural sub-score + Chemical matter sub-score + Modality sub-score
   • Pathway: Pathway centrality sub-score + Expression sub-score + Animal model sub-score

6. Output. Present a detailed profile card for the target including:

   • Summary table with all scores
   • Strengths / Weaknesses / Opportunities / Threats (SWOT) analysis
   • Comparison across diseases if multiple provided
   • Recommended next experiments

7. Generate HTML report. Run the Python script to produce the interactive HTML matrix:

   python open_targets_client.py validate "TARGET" "DISEASE1,DISEASE2"
   

   This creates results/validation_{disease}.html — the interactive scoring matrix. Tell the user to open it in their browser.

8. Save narrative to results/{TARGET}_deep_dive.md alongside the HTML.