qsong-github/drugclaw/unid3

UniD3 - Drug Discovery Knowledge Graph

Overview

UniD3 is a multi-knowledge-graph built from 150,000+ PubMed articles, stored as 6 GraphML files. It supports drug-disease matching, effectiveness assessment, and drug-target analysis.

• Source: https://github.com/QSong-github/UniD3
• Local path: resources_metadata/drug_knowledgebase/UniD3
• Format: GraphML (6 files, e.g. UniD3_L1T1.graphml)
• Dependency: networkx

Node Schema

Each node contains:

Edge Schema

Each edge contains:

API Reference

list_graphs() → list[str]

Return names of all 6 GraphML files.

from UniD3 import list_graphs
list_graphs()
# → ["UniD3_L1T1", "UniD3_L1T2", "UniD3_L2T1", ...]

query_entities(entities, graph_names=None) → list[dict]

Look up one or more entities by name (case-insensitive).

from UniD3 import query_entities
# Single entity
query_entities("RESPIRATORY DISEASES")
# Multiple entities
query_entities(["CALVES", "INFLAMMATION MODULATION"])
# Restrict to specific graph
query_entities("CALVES", graph_names=["UniD3_L1T1"])

Returns list of dicts: {entity, entity_type, description, source_id, graph}

get_neighbors(entity, graph_names=None) → list[dict]

Get all direct neighbors and connecting edge info for an entity.

from UniD3 import get_neighbors
get_neighbors("CALVES")

Returns list of dicts: {graph, neighbor: {entity, entity_type, description, source_id}, edge: {source, target, weight, description, keywords, source_id}}

search_by_type(entity_type, graph_names=None, limit=50) → list[dict]

Filter entities by type.

from UniD3 import search_by_type
search_by_type("DISEASE", limit=10)
search_by_type("DRUG", graph_names=["UniD3_L1T1"])

search_by_keyword(keyword, graph_names=None, limit=50) → list[dict]

Substring match over entity names and descriptions.

from UniD3 import search_by_keyword
search_by_keyword("inflammation")
search_by_keyword("cancer", limit=20)

Typical Workflow

from UniD3 import query_entities, get_neighbors, search_by_type
# Step 1: Find a drug entity
hits = query_entities("ASPIRIN")
# Step 2: Explore its neighborhood (related diseases, targets, etc.)
neighbors = get_neighbors("ASPIRIN")
# Step 3: Filter neighbors by type
diseases = [n for n in neighbors if n["neighbor"]["entity_type"] == "DISEASE"]