--- name: drugbank-database-65132e86fea1 description: Programmatic access to DrugBank drug and target data; use when you need to download, parse, and analyze DrugBank XML for properties, interactions, pathways, and pharmacology. license: MIT author: AIPOCH --- > **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) ## When to Use - You need to extract structured drug properties (e.g., identifiers, synonyms, ATC codes) from DrugBank XML for downstream analysis. - You want to build and analyze drug–drug interaction (DDI) networks from DrugBank interaction records. - You are mapping drugs to targets (proteins/genes) to support target discovery, mechanism-of-action analysis, or enrichment workflows. - You need to connect drugs to pathways and pharmacology annotations for systems pharmacology or knowledge graph construction. - You want to generate tabular datasets (CSV/Parquet) from DrugBank for use in notebooks, dashboards, or ML pipelines. ## Key Features - Programmatic download of DrugBank releases via `drugbank-downloader` (requires DrugBank access). - XML parsing and traversal using `lxml` for reliable extraction of nested DrugBank entities. - Data wrangling into `pandas` DataFrames for filtering, joining, and export. - Network construction and analysis with `networkx` (e.g., DDI graphs, drug–target bipartite graphs). - Optional cheminformatics support with `rdkit` for structure-based processing (e.g., SMILES/InChI handling when present). ## Dependencies - `drugbank-downloader` (version varies by your environment) - `lxml>=4.9` - `pandas>=2.0` - `networkx>=3.0` - `rdkit>=2022.09` (optional; required only for structure/chemistry workflows) ## Example Usage ```python """ End-to-end example: 1) Parse a local DrugBank XML file 2) Extract a minimal drug table 3) Extract drug-drug interactions 4) Build a DDI graph Prerequisites: - You must obtain DrugBank XML via your DrugBank account/license. - Place the XML file at ./drugbank.xml (or update the path). """ from lxml import etree import pandas as pd import networkx as nx DRUGBANK_XML_PATH = "./drugbank.xml" NS = {"db": "http://www.drugbank.ca"} # DrugBank XML namespace # --- Parse XML --- tree = etree.parse(DRUGBANK_XML_PATH) root = tree.getroot() # --- Extract drug records (minimal fields) --- drugs = [] for drug in root.xpath("//db:drug", namespaces=NS): drugbank_id = drug.xpath("string(db:drugbank-id[@primary='true'])", namespaces=NS).strip() name = drug.xpath("string(db:name)", namespaces=NS).strip() drug_type = drug.get("type", "").strip() # Optional: first SMILES if present smiles = drug.xpath( "string(db:calculated-properties/db:property[db:kind='SMILES']/db:value)", namespaces=NS, ).strip() drugs.append( { "drugbank_id": drugbank_id, "name": name, "type": drug_type, "smiles": smiles or None, } ) drugs_df = pd.DataFrame(drugs).dropna(subset=["drugbank_id"]) print("Drugs:", len(drugs_df)) print(drugs_df.head()) # --- Extract drug-drug interactions --- interactions = [] for drug in root.xpath("//db:drug", namespaces=NS): src_id = drug.xpath("string(db:drugbank-id[@primary='true'])", namespaces=NS).strip() src_name = drug.xpath("string(db:name)", namespaces=NS).strip() for ddi in drug.xpath("db:drug-interactions/db:drug-interaction", namespaces=NS): tgt_id = ddi.xpath("string(db:drugbank-id)", namespaces=NS).strip() tgt_name = ddi.xpath("string(db:name)", namespaces=NS).strip() description = ddi.xpath("string(db:description)", namespaces=NS).strip() if src_id and tgt_id: interactions.append( { "source_id": src_id, "source_name": src_name, "target_id": tgt_id, "target_name": tgt_name, "description": description or None, } ) ddi_df = pd.DataFrame(interactions) print("Interactions:", len(ddi_df)) print(ddi_df.head()) # --- Build a DDI graph --- G = nx.from_pandas_edgelist( ddi_df, source="source_id", target="target_id", edge_attr=["description"], create_using=nx.Graph(), ) print("DDI graph nodes:", G.number_of_nodes()) print("DDI graph edges:", G.number_of_edges()) # Example analysis: top 10 drugs by interaction degree top_degree = sorted(G.degree, key=lambda x: x[1], reverse=True)[:10] top_degree_df = pd.DataFrame(top_degree, columns=["drugbank_id", "degree"]).merge( drugs_df[["drugbank_id", "name"]], on="drugbank_id", how="left", ) print(top_degree_df) ``` ## Implementation Details - **Access & authentication** - DrugBank data access requires a free academic account or a paid license depending on your use case. - The `drugbank-downloader` step is responsible for fetching the release artifacts; ensure you comply with DrugBank terms. - **XML parsing approach** - DrugBank is distributed as a large XML document; `lxml.etree` is used for robust XPath-based extraction. - The XML uses a namespace (commonly `http://www.drugbank.ca`); XPath queries must include the namespace mapping (e.g., `NS = {"db": "http://www.drugbank.ca"}`). - **Core extraction patterns** - **Primary DrugBank ID**: `db:drugbank-id[@primary='true']` - **Drug name**: `db:name` - **Calculated properties (e.g., SMILES)**: `db:calculated-properties/db:property[db:kind='SMILES']/db:value` - **Drug interactions**: `db:drug-interactions/db:drug-interaction` with fields `db:drugbank-id`, `db:name`, `db:description` - **Data modeling** - Use `pandas` DataFrames for normalized tables (drugs, targets, interactions, pathways). - Use `networkx` for graph representations: - DDI graph: nodes are drugs, edges are interactions (store `description` as edge attribute). - Drug–target graph: bipartite graph with drug nodes and target nodes. - **Performance considerations** - DrugBank XML can be large; for memory-sensitive environments, consider iterative parsing (`etree.iterparse`) and writing intermediate results to disk. - Normalize identifiers early (e.g., always keep primary DrugBank IDs) to simplify joins across tables. - **Further references** - See: `references/data-access.md` - See: `references/drug-queries.md` - See: `references/interactions.md` --- Source attribution: aipoch Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Evidence%20Insight/drugbank-database/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 AIpoch Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: aipoch Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Evidence%20Insight/drugbank-database/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Data Scientist, ML / AI Engineer, Researcher Specialties: API, Database Tools: Drugbank Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e8d579bd756b93f70109eda8c6b709448e978235b487ab6ba489e5e933bbfae8