knowledge-graph-tools

name

knowledge-graph-tools

description

Drug-discovery knowledge-graph workflow guide for assembling drug-target-disease-pathway relationship graphs from OpenTargets GraphQL, ChEMBL REST, STRING PPI, and Reactome pathway APIs, then running hub detection, shortest-path queries, and neighborhood expansion with networkx. Use when the user asks to build, query, or visualize a biomedical knowledge graph connecting drugs, targets, diseases, and pathways from real public databases without making clinical claims.

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.