Knowledge Graph Tools

Knowledge Graph Tools

Use this skill for building and querying biomedical relationship graphs from public drug-discovery APIs, not for clinical decision-making.

Typical triggers:

• build a knowledge graph seeded from a disease, a drug, or a target list
• find shortest paths from a drug to a disease through intermediate targets and pathways
• identify hub targets that bridge multiple disease areas or drug mechanisms
• expand the neighborhood around a protein to see connected drugs, diseases, and pathways
• merge OpenTargets, ChEMBL, STRING, and Reactome data into one queryable graph

Working Rules

1. Every node gets a typed label: drug, target, disease, or pathway.
2. Every edge records its source database and, where available, an evidence score.
3. Use canonical identifiers: Ensembl for targets, ChEMBL for drugs, EFO for diseases, Reactome stable IDs for pathways.
4. Hub analysis reflects database connectivity, not biological importance; well-studied proteins dominate.
5. Shortest-path hypotheses are topological leads, not validated biology.
6. Do not claim causal or therapeutic conclusions from graph structure alone.

Environment Check

which python3 || true
python3 - <<'PY'
mods = ["networkx", "requests"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If networkx or requests is missing, say so immediately. If network access is blocked, only the query mode on pre-built GraphML files will work.

Bundled Assets

• templates/knowledge_graph.py

Build: Disease-Centric Graph

Use templates/knowledge_graph.py --mode build --seed-type disease for:

• fetching disease-associated targets from OpenTargets
• fetching known drugs for those targets from OpenTargets
• adding protein-protein interactions from STRING
• adding pathway membership from Reactome
• assembling typed nodes and edges into a single graph

Quick start:

python3 templates/knowledge_graph.py \
  --mode build \
  --seed-type disease \
  --seed "Crohn's disease" \
  --max-targets 30 \
  --include-string \
  --include-reactome \
  --output kg/crohn_graph.graphml \
  --summary kg/crohn_summary.json

Deliverables:

• GraphML file with typed nodes (entity_type) and typed edges (relation, source_db, score)
• summary JSON with node/edge counts by type, top hubs, and data sources queried

Build: Drug-Centric Graph

Use --seed-type drug to start from a drug and expand through its targets:

python3 templates/knowledge_graph.py \
  --mode build \
  --seed-type drug \
  --seed "imatinib" \
  --max-targets 20 \
  --include-string \
  --include-reactome \
  --output kg/imatinib_graph.graphml \
  --summary kg/imatinib_summary.json

Query: Shortest Path

Use --mode query --query-type shortest-path on an existing GraphML file:

python3 templates/knowledge_graph.py \
  --mode query \
  --input kg/crohn_graph.graphml \
  --query-type shortest-path \
  --from-node "CHEMBL941" \
  --to-node "EFO_0000384" \
  --summary kg/path_result.json

Deliverables:

• summary JSON with path length, node sequence, and edge relations for each step

Query: Hub Analysis

Use --mode query --query-type hubs:

python3 templates/knowledge_graph.py \
  --mode query \
  --input kg/crohn_graph.graphml \
  --query-type hubs \
  --top-k 20 \
  --summary kg/hub_targets.json

Deliverables:

• summary JSON with top-K nodes ranked by degree and betweenness centrality, with entity type

Query: Neighborhood Expansion

Use --mode query --query-type neighbors:

python3 templates/knowledge_graph.py \
  --mode query \
  --input kg/crohn_graph.graphml \
  --query-type neighbors \
  --center-node "ENSG00000141510" \
  --radius 2 \
  --summary kg/tp53_neighborhood.json

Deliverables:

• summary JSON with subgraph node list, edge list, and entity-type breakdown

Output Expectations

Good answers should mention:

• seed entity and type (drug, disease, or target list)
• which APIs were queried (OpenTargets, ChEMBL, STRING, Reactome)
• graph size: node count by type, edge count by relation type
• for hub queries: top hub identifiers, degrees, and entity types
• for path queries: full path with intermediate nodes and edge types
• identifier schemes used
• where GraphML and JSON were saved

Related Skills

For compound and regulatory database lookups from ChEMBL, openFDA, ClinicalTrials.gov, activate pharma-db-tools. For target-specific intelligence dossiers, activate target-intelligence-tools. For drug repurposing hypothesis generation, activate drug-repurposing-tools. For pathway enrichment from gene lists, activate pathway-enrichment-tools. For network pharmacology analysis, activate network-pharmacology-tools. For raw bio database lookups in UniProt, PDB, ClinVar, gnomAD, Reactome, STRING, activate bio-db-tools.

Reference

This skill queries the following public APIs during build mode:

• OpenTargets Platform GraphQL — https://api.platform.opentargets.org/api/v4/graphql — disease-target associations (associatedTargets), known drugs (knownDrugs), and entity search (platform.opentargets.org)
• ChEMBL REST API — https://www.ebi.ac.uk/chembl/api/data — molecule search, mechanism-of-action retrieval, and target cross-references (chembl.gitbook.io)
• STRING API v12 — https://version-12-0.string-db.org/api — protein-protein interaction partners with combined confidence scores (string-db.org)
• Reactome Content Service — https://reactome.org/ContentService — pathway search by gene symbol with species filter (reactome.org)
• Graph analysis uses networkx — nx.shortest_path, nx.betweenness_centrality, nx.ego_graph (networkx.org)
• The target-intelligence-tools skill in this repository served as the reference implementation for API calling patterns, error handling, and identifier resolution.