Chembl Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The ChEMBL database skill provides a structured interface to query the EBI ChEMBL database for information on bioactive molecules, drug targets, bioactivity data, and chemical structures. It utilizes a dedicated utility script to manage API interactions, ensuring rate limiting and error handling are managed correctly. The skill supports various operations including searching for molecules by name, fetching detailed target information, retrieving bioactivity data with unit normalization, and performing server-side structure-based searches like similarity and substructure matching.
What it does
- Fetches detailed information for molecules, targets, and approved drugs using ChEMBL IDs.
- Performs free-text searches for molecules, targets, and bioactivity data.
- Retrieves and normalizes bioactivity data (e.g., IC50, Ki) to nM units for consistent analysis.
- Executes server-side similarity and substructure searches using SMILES strings.
- Downloads 2D chemical structure images in SVG or PNG formats.
- Cross-references ChEMBL entities with external databases like UniProt.
When to use it
- When you need to retrieve bioactivity data for specific drug targets or compounds.
- When performing structure-based searches to find similar molecules or substructures.
- When investigating drug mechanisms of action or clinical indications.
- When you need to download chemical structure diagrams for research documentation.
- When mapping protein targets to UniProt accessions or other cross-references.
Advantages
- Ensures consistent API interaction through a centralized utility script.
- Supports server-side structure searching without requiring local cheminformatics software.
- Provides built-in unit normalization for bioactivity data.
- Enables easy integration with other tools by outputting results to JSON files.
- Facilitates batch processing and pagination for large datasets.
Usage examples
- “Find the target information for EGFR.”
- The agent uses the target search command to retrieve a list of EGFR-related targets and their corresponding ChEMBL IDs, saving the results to a JSON file.
- “Get all IC50 values for CHEMBL203 normalized to nM.”
- The agent queries the activity endpoint with filters for the target ID and standard type, applying the normalization flag to produce a file with consistent nM values.
- “Find molecules similar to aspirin (SMILES: CC(=O)Oc1ccccc1C(=O)O) with 85% similarity.”
- The agent executes a similarity search against the ChEMBL database and saves the matching molecules to a specified output file.
- “Download a 2D structure image for CHEMBL25.”
- The agent generates an SVG image file of the molecule structure for use in reports or presentations.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/chembl-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip chembl-database.zip -d .agents/skills
Documentation by Google DeepMind
ChEMBL Database Query
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/chembl_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://chembl.gitbook.io/chembl-interface-documentation/about, then (2) create the file recording the notification text and timestamp.
Core Rules
[!IMPORTANT] Use the Utility Scripts: You MUST ALWAYS use the provided utility script
scripts/chembl_api.pyfor all ChEMBL API interactions, including checking status. NEVER usecurlor custom Python requests to query the ChEMBL API directly. This ensures rate limit is enfoced and also retries on network errors.Output to File (Required): The
--outputflag is required for every subcommand. All JSON results are written to the specified file. After running the command, read the output file with jq or your own code to extract the data. List results are typically wrapped in a JSON array keyed by the endpoint name (e.g.,molecules,activities).Notification: If this skill is used, ensure this is mentioned in the output.
Utility Script
All ChEMBL API queries use one script with subcommands:
uv run scripts/chembl_api.py <subcommand> --output <file> [options]1. Check API Status
uv run scripts/chembl_api.py status --output /tmp/status.json2. Molecule Queries
Fetch by ChEMBL ID: bash uv run scripts/chembl_api.py molecule --id CHEMBL25 --output /tmp/mol.json
Search by name: bash uv run scripts/chembl_api.py molecule --search "aspirin" --limit 3 --output /tmp/mol_search.json
Batch fetch: bash uv run scripts/chembl_api.py molecule --ids "CHEMBL25;CHEMBL1642" --limit 10 --output /tmp/mol_batch.json
Filter by properties: bash uv run scripts/chembl_api.py molecule --filter molecule_properties__mw_freebase__lte=500 --limit 5 --output /tmp/mol_filter.json
Filter by range: bash uv run scripts/chembl_api.py molecule --filter molecule_properties__mw_freebase__range=150,200 --limit 5 --output /tmp/mol_range.json
Download SDF structure file: bash uv run scripts/chembl_api.py molecule --id CHEMBL25 --dl_format sdf --output /tmp/aspirin.sdf
Tip: SDF/MOL files can be passed directly to tools like PyMOL or RDKit for 3D visualization and analysis.
3. Target Queries
Search for targets: bash uv run scripts/chembl_api.py target --search "EGFR" --limit 5 --output /tmp/targets.json
Fetch by ID: bash uv run scripts/chembl_api.py target --id CHEMBL203 --output /tmp/egfr.json
4. Bioactivity Data
Fetch activity by ID: bash uv run scripts/chembl_api.py activity --id 31863 --output /tmp/act.json
Search activities: bash uv run scripts/chembl_api.py activity --search "EGFR" --limit 5 --output /tmp/act_search.json
Filter activities for a target: bash uv run scripts/chembl_api.py activity --filter target_chembl_id=CHEMBL203 standard_type=IC50 --limit 10 --output /tmp/egfr_ic50.json
Normalize bioactivity units to nM: bash uv run scripts/chembl_api.py activity --filter target_chembl_id=CHEMBL203 standard_type=IC50 --limit 5 --normalize --output /tmp/egfr_normalized.json
Important: Bioactivity values come in various units (nM, µM, pM). Use
--normalizeto convert all values to nM for consistent comparison. Each record will includenormalized_value_nMandnormalization_note.
5. Drug Information
Fetch drug details: bash uv run scripts/chembl_api.py drug --id CHEMBL25 --output /tmp/drug.json
Drug indications: bash uv run scripts/chembl_api.py drug_indication --filter molecule_chembl_id=CHEMBL25 --limit 10 --output /tmp/indications.json
Filter indications by phase: bash uv run scripts/chembl_api.py drug_indication --filter molecule_chembl_id=CHEMBL25 max_phase_for_ind=4.0 --limit 10 --output /tmp/approved_indications.json
Drug warnings: bash uv run scripts/chembl_api.py drug_warning --limit 5 --output /tmp/warnings.json
Mechanisms of action: bash uv run scripts/chembl_api.py mechanism --filter molecule_chembl_id=CHEMBL25 --limit 5 --output /tmp/mech.json
6. Structure-Based Searches
Note: Both similarity and substructure searches are performed server-side on ChEMBL's pre-indexed database. They do not require a local RDKit installation.
Similarity search (SMILES + threshold): bash uv run scripts/chembl_api.py similarity --smiles "CC(=O)Oc1ccccc1C(=O)O" --similarity 85 --limit 5 --output /tmp/similar.json
Substructure search (SMILES): bash uv run scripts/chembl_api.py substructure --smiles "c1ccccc1" --limit 5 --output /tmp/substruct.json
7. Compound Image
Download a 2D structure image (SVG by default, scalable for publication):
uv run scripts/chembl_api.py image --id CHEMBL25 --output /tmp/chembl25.svgOptions:
--dimensions: Image size in pixels (max 500, default 500).--engine: Rendering engine (default: rdkit).--img_format: Output format —svg(default, vector) orpng(raster).
8. Cross-Referencing with Other Databases
ChEMBL integrates with UniProt, Ensembl, PubChem, and other databases. Common cross-referencing patterns:
Find a ChEMBL target from a UniProt accession: bash uv run scripts/chembl_api.py target --filter target_components__accession=P00533 --limit 5 --output /tmp/uniprot_target.json
Resolve any ChEMBL ID to its entity type: bash uv run scripts/chembl_api.py chembl_id_lookup --id CHEMBL203 --output /tmp/lookup.json
Look up cross-reference sources: bash uv run scripts/chembl_api.py xref_source --limit 10 --output /tmp/xrefs.json
Tip: Use the
target_componentendpoint to find UniProt accessions, gene names, and protein sequences for any ChEMBL target.
9. Pagination
All list endpoints support --limit and --offset for pagination:
# First page: 2 results starting at offset 0
uv run scripts/chembl_api.py molecule --limit 2 --offset 0 --output /tmp/page1.json
# Second page: next 2 results starting at offset 2
uv run scripts/chembl_api.py molecule --limit 2 --offset 2 --output /tmp/page2.jsonThe response includes page_meta with total_count, limit, offset, next,
and previous links. Use successive --offset values to page through large
result sets.
10. Other Endpoints
All remaining endpoints follow the same pattern:
uv run scripts/chembl_api.py <subcommand> --output <file> [--id ID | --ids ID1;ID2 | --search QUERY] [--limit N] [--offset N] [--filter KEY=VAL ...]Key subcommands at a glance:
molecule(searchable: true): Molecules/compounds — the primary entry pointtarget(searchable: true): Drug targets (proteins, organisms, etc.)activity(searchable: true): Bioactivity data (IC50, Ki, EC50, etc.)drug(searchable: false): Approved drugsmechanism(searchable: false): Mechanisms of actionassay(searchable: true): Assay descriptionssimilarity(searchable: false): Similarity search (special)substructure(searchable: false): Substructure search (special)image(searchable: false): Compound image download (special)
Full subcommand list:
activity_supp(searchable: false): Supplementary activity dataassay_class(searchable: false): Assay classificationsatc_class(searchable: false): ATC drug classificationsbinding_site(searchable: false): Binding site informationbiotherapeutic(searchable: false): Biotherapeutic moleculescell_line(searchable: false): Cell line detailschembl_id_lookup(searchable: true): ChEMBL ID resolutionchembl_release(searchable: false): Database release infocompound_record(searchable: false): Compound recordscompound_structural_alert(searchable: false): Structural alertsdocument(searchable: true): Literature documentsdocument_similarity(searchable: false): Document similaritydrug_indication(searchable: false): Drug indicationsdrug_warning(searchable: false): Drug safety warningsgo_slim(searchable: false): GO slim termsmetabolism(searchable: false): Metabolism datamolecule_form(searchable: false): Molecule forms (salts/parents)organism(searchable: false): Organismsprotein_classification(searchable: true): Protein classificationssource(searchable: false): Data sourcestarget_component(searchable: false): Target protein componentstarget_relation(searchable: false): Target relationshipstissue(searchable: false): Tissue typesxref_source(searchable: false): Cross-reference sourcesstatus(searchable: false): API status check (special)
Common Options
--output FILE: Required. Output file path for JSON results.--id ID: Fetch a single record by ID.--ids ID1;ID2;...: Batch fetch multiple records.--search QUERY: Free-text search (only for searchable endpoints, marked ✓).--limit N: Max results to return (default: 5).--offset N: Pagination offset.--filter KEY=VAL: Filter parameters (can specify multiple).--normalize: (activity only) Normalize values to nM.--dl_format sdf|mol: (molecule only) Download structure file.
Reference
API Endpoints Reference: See references/api_endpoints.md for the full list of endpoints and filter operators.
Workflow
Use
status --output /tmp/status.jsonto verify the API is available.Search for targets, molecules, or drugs using the relevant subcommand.
Read the output JSON file to extract IDs and data.
Use IDs from search results to fetch detailed records.
Query
activitywith filters to get bioactivity data for targets/molecules. Use--normalizewhen comparing values across studies.Use
similarityorsubstructurefor server-side structure-based queries.Download compound images with
imageor structure files withmolecule --dl_format sdf.Use
target --filter target_components__accession=<UniProt>to cross- reference with UniProt.
Files
- ATTRIBUTION.mdAdded by AutoGineer912 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md11.2 KiB
- references/api_endpoints.md2.8 KiB
- references/citation.bib3.6 KiB
- scripts/chembl_api.pyScript18.4 KiB
scripts/chembl_api.py
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ChEMBL REST API client CLI.
CLI tool covering all ChEMBL web services API endpoints. Writes JSON output to
a file specified by --output. Enforces rate limiting between requests and
retries on transient errors (HTTP 429, 503) with exponential backoff.
"""
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "polite-http",
# ]
# ///
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Any
import urllib.parse
from polite_http import http_client
BASE_URL = "https://www.ebi.ac.uk/chembl/api/data"
_CLIENT = http_client.HttpClient(BASE_URL, qps=5.0)
_LICENSE_NOTICE = (
"Data from the ChEMBL Database. You MUST notify the user"
" that this data comes from ChEMBL and advise them to"
" review the ChEMBL licensing terms."
)
SEARCHABLE_ENDPOINTS = frozenset([
"activity",
"assay",
"chembl_id_lookup",
"document",
"molecule",
"protein_classification",
"target",
])
ENDPOINT_MAP = {
"activity": "activity",
"activity_supp": "activity_supplementary_data_by_activity",
"assay": "assay",
"assay_class": "assay_class",
"atc_class": "atc_class",
"binding_site": "binding_site",
"biotherapeutic": "biotherapeutic",
"cell_line": "cell_line",
"chembl_id_lookup": "chembl_id_lookup",
"chembl_release": "chembl_release",
"compound_record": "compound_record",
"compound_structural_alert": "compound_structural_alert",
"document": "document",
"document_similarity": "document_similarity",
"drug": "drug",
"drug_indication": "drug_indication",
"drug_warning": "drug_warning",
"go_slim": "go_slim",
"mechanism": "mechanism",
"metabolism": "metabolism",
"molecule": "molecule",
"molecule_form": "molecule_form",
"organism": "organism",
"protein_classification": "protein_classification",
"source": "source",
"target": "target",
"target_component": "target_component",
"target_relation": "target_relation",
"tissue": "tissue",
"xref_source": "xref_source",
}
UNIT_CONVERSION_TO_NM = {
"nm": 1.0,
"um": 1e3,
"µm": 1e3,
"mm": 1e6,
"m": 1e9,
"pm": 1e-3,
"fm": 1e-6,
}
def _write_json(data: Any, output_path: str) -> None:
"""Write a Python object as indented JSON to the given file path.
Creates parent directories if they do not exist. Prints a short
confirmation to stdout so the agent knows where the file was saved.
Args:
data: The Python object to serialize to JSON.
output_path: The file path to write the JSON output to.
"""
out_dir = os.path.dirname(output_path)
if out_dir:
os.makedirs(out_dir, exist_ok=True)
if isinstance(data, dict):
data["_license_notice"] = (
"Data from the ChEMBL Database. Please review the licensing terms at"
" https://www.ebi.ac.uk/chembl/"
)
with open(output_path, "w") as f:
json.dump(data, f, indent=2)
f.write("\n")
print(
json.dumps(
{
"status": "success",
"output_file": output_path,
"size_bytes": os.path.getsize(output_path),
"license_notice": _LICENSE_NOTICE,
},
indent=2,
)
)
def _make_request(url: str) -> dict[str, Any]:
"""Send an HTTP GET to *url* and return the parsed JSON response.
Uses HttpClient for retry logic. Non-retryable HTTP errors
are returned as a dict with ``status=error``.
Args:
url: The URL to send the HTTP GET request to.
Returns:
The parsed JSON response as a dict or list, or a dict with
``status=error`` and a ``message`` key on failure.
"""
try:
return _CLIENT.fetch_json(url)
except http_client.HttpError as e:
return {
"status": "error",
"http_code": e.status_code,
"message": str(e),
"detail": (
e.body.decode("utf-8", errors="replace")[:500] if e.body else ""
),
}
except Exception as e:
return {
"status": "error",
"message": f"Network error: {e}",
}
def _download_binary(url: str, output_path: str) -> dict[str, Any]:
"""Download binary content from *url* and save to *output_path*.
Used for image and structure-file downloads. Uses HttpClient
for retry logic.
Args:
url: The URL to download binary content from.
output_path: The file path to save the downloaded content to.
Returns:
A dict with ``status=success``, ``message``, and ``size_bytes`` on
success, or ``status=error`` and ``message`` on failure.
"""
try:
content = _CLIENT.fetch_bytes(url)
out_dir = os.path.dirname(output_path)
if out_dir:
os.makedirs(out_dir, exist_ok=True)
with open(output_path, "wb") as f:
f.write(content)
return {
"status": "success",
"message": f"Saved to {output_path}",
"size_bytes": len(content),
"license_notice": _LICENSE_NOTICE,
}
except Exception as e:
return {"status": "error", "message": f"File system/Network error: {e}"}
def _build_url(
endpoint: str,
resource_id: str | None = None,
ids_list: str | None = None,
search: str | None = None,
filters: list[str] | None = None,
limit: int | None = None,
offset: int | None = None,
) -> str:
"""Construct a ChEMBL API URL from components.
Handles single-resource, batch (set/), search, filter, and
pagination parameters.
Args:
endpoint: The ChEMBL API endpoint name (e.g., "molecule").
resource_id: A single ChEMBL ID or numeric ID.
ids_list: Semicolon-separated string of IDs for batch fetching.
search: Free-text search query string.
filters: List of "KEY=VALUE" filter strings.
limit: Maximum number of results to return.
offset: Pagination offset.
Returns:
A complete URL string for the ChEMBL API request.
Raises:
SystemExit: If a filter string does not contain '='.
"""
parts = [BASE_URL, endpoint]
if resource_id:
parts.append(resource_id)
elif ids_list:
parts.append("set/" + ids_list)
elif search:
parts.append("search")
url = "/".join(parts) + ".json"
params: dict[str, Any] = {}
if search:
params["q"] = search
if limit is not None:
params["limit"] = limit
if offset is not None:
params["offset"] = offset
if filters:
for filt in filters:
if "=" not in filt:
print(
json.dumps(
{
"status": "error",
"message": (
f"Invalid filter '{filt}': expected KEY=VALUE format."
),
},
indent=2,
),
)
sys.exit(1)
key, val = filt.split("=", 1)
params[key] = val
if params:
url += "?" + urllib.parse.urlencode(params)
return url
def _normalize_activity(record: dict[str, Any]) -> dict[str, Any]:
"""Add normalised nM value to a single activity record.
Reads ``standard_value`` and ``standard_units``, converts to nM
using UNIT_CONVERSION_TO_NM, and adds ``normalized_value_nM`` and
``normalization_note`` keys to *record*.
Args:
record: An activity record dict containing ``standard_value`` and
``standard_units`` keys.
Returns:
The mutated *record* dict with ``normalized_value_nM`` and
``normalization_note`` keys added.
"""
units = (record.get("standard_units") or "").strip().lower()
value = record.get("standard_value")
if value is None or units not in UNIT_CONVERSION_TO_NM:
record["normalized_value_nM"] = None
record["normalization_note"] = (
f"Cannot normalize: units='{record.get('standard_units')}'"
if value is not None
else "No standard_value"
)
return record
try:
nm_value = float(value) * UNIT_CONVERSION_TO_NM[units]
record["normalized_value_nM"] = nm_value
record["normalization_note"] = (
f"Converted {value} {record.get('standard_units')} -> {nm_value} nM"
)
except (ValueError, TypeError):
record["normalized_value_nM"] = None
record["normalization_note"] = f"Cannot convert value: {value}"
return record
def cmd_generic(args: argparse.Namespace) -> None:
"""Handle all standard endpoint subcommands (molecule, target, etc.).
Builds the URL, makes the request, optionally normalises activity
values, and writes JSON output to args.output.
Args:
args: Parsed command-line arguments. Expected attributes: command, search,
id, ids, filter, limit, offset, output, and optionally normalize (for
activity endpoint).
"""
api_endpoint = ENDPOINT_MAP[args.command]
can_search = api_endpoint in SEARCHABLE_ENDPOINTS
if args.search and not can_search:
searchable_list = ", ".join(sorted(SEARCHABLE_ENDPOINTS))
error = {
"status": "error",
"message": f"Search is not supported for endpoint '{args.command}'.",
"suggestion": (
f"Use --filter instead (e.g. {args.command} --filter"
" KEY=VALUE), or search a supported endpoint:"
f" {searchable_list}."
),
}
_write_json(error, args.output)
sys.exit(1)
url = _build_url(
api_endpoint,
resource_id=args.id,
ids_list=args.ids,
search=args.search if can_search else None,
filters=args.filter,
limit=args.limit if not args.id else None,
offset=args.offset if not args.id else None,
)
result = _make_request(url)
normalize = getattr(args, "normalize", False)
if normalize and api_endpoint == "activity":
if isinstance(result, dict) and "activities" in result:
result["activities"] = [
_normalize_activity(r) for r in result["activities"]
]
elif isinstance(result, dict) and "standard_value" in result:
result = _normalize_activity(result)
_write_json(result, args.output)
def cmd_status(args: argparse.Namespace) -> None:
"""Check ChEMBL API status and write the result to args.output.
Args:
args: Parsed command-line arguments. Expected attribute: output.
"""
url = f"{BASE_URL}/status.json"
result = _make_request(url)
_write_json(result, args.output)
def cmd_similarity(args: argparse.Namespace) -> None:
"""Run a server-side similarity search against the ChEMBL database.
Requires --smiles and --similarity (threshold 0-100). Writes
matching molecules to args.output.
Args:
args: An argparse.Namespace object containing the parsed command-line
arguments. Expected attributes: smiles, similarity, limit, offset, output.
"""
smiles_encoded = urllib.parse.quote(args.smiles, safe="")
url = f"{BASE_URL}/similarity/{smiles_encoded}/{args.similarity}.json"
params: dict[str, Any] = {}
if args.limit:
params["limit"] = args.limit
if args.offset:
params["offset"] = args.offset
if params:
url += "?" + urllib.parse.urlencode(params)
result = _make_request(url)
_write_json(result, args.output)
def cmd_substructure(args: argparse.Namespace) -> None:
"""Run a server-side substructure search against ChEMBL.
Requires --smiles. Writes matching molecules to args.output.
Args:
args: An argparse.Namespace object containing the parsed command-line
arguments. Expected attributes: smiles, limit, offset, and output.
"""
smiles_encoded = urllib.parse.quote(args.smiles, safe="")
url = f"{BASE_URL}/substructure/{smiles_encoded}.json"
params: dict[str, Any] = {}
if args.limit:
params["limit"] = args.limit
if args.offset:
params["offset"] = args.offset
if params:
url += "?" + urllib.parse.urlencode(params)
result = _make_request(url)
_write_json(result, args.output)
def cmd_image(args: argparse.Namespace) -> None:
"""Download a compound 2D structure image (SVG or PNG).
Binary content is saved to args.output. A JSON status summary
is printed to stdout.
Args:
args: An argparse.Namespace object containing the parsed command-line
arguments. Expected attributes: id, output, dimensions, engine, img_format
"""
url = f"{BASE_URL}/image/{args.id}"
params: dict[str, Any] = {}
if args.dimensions:
params["dimensions"] = args.dimensions
if args.engine:
params["engine"] = args.engine
if args.img_format:
params["format"] = args.img_format
if params:
url += "?" + urllib.parse.urlencode(params)
result = _download_binary(url, args.output)
print(json.dumps(result, indent=2))
def cmd_molecule_download(args: argparse.Namespace) -> None:
"""Download a molecule structure file in SDF or MOL format.
Requires --id and --dl_format. Binary content is saved to
args.output (or defaults to <CHEMBL_ID>.<format>).
Args:
args: An argparse.Namespace object containing the parsed command-line
arguments. Expected attributes: id, dl_format, output.
"""
fmt = args.dl_format
chembl_id = args.id
if not chembl_id:
error = {
"status": "error",
"message": "--id is required for --dl_format",
}
print(json.dumps(error, indent=2))
sys.exit(1)
url = f"{BASE_URL}/molecule/{chembl_id}.{fmt}"
output = args.output if args.output else f"{chembl_id}.{fmt}"
result = _download_binary(url, output)
print(json.dumps(result, indent=2))
def _add_common_args(parser: argparse.ArgumentParser) -> None:
"""Add shared arguments (--id, --ids, --search, etc.) to a subparser.
Args:
parser: The argparse subparser to add arguments to.
"""
parser.add_argument("--id", type=str, help="Single ChEMBL ID or numeric ID")
parser.add_argument(
"--ids",
type=str,
help="Semicolon-separated list of IDs for batch fetch",
)
parser.add_argument(
"--search",
type=str,
help="Free-text search query (only for searchable endpoints)",
)
parser.add_argument(
"--limit",
type=int,
default=5,
help="Max results to return (default: 5)",
)
parser.add_argument(
"--offset",
type=int,
default=None,
help="Pagination offset",
)
parser.add_argument(
"--filter",
type=str,
nargs="*",
help="Filter as KEY=VALUE pairs",
)
parser.add_argument(
"--output",
type=str,
required=True,
help="Output JSON file path (required)",
)
def build_parser() -> argparse.ArgumentParser:
"""Build the top-level argparse parser with all subcommands.
Creates subparsers for every ChEMBL endpoint plus the special
status, similarity, substructure, and image subcommands.
Returns:
An argparse.ArgumentParser instance configured with all ChEMBL API
subcommands.
"""
parser = argparse.ArgumentParser(
description=(
"ChEMBL REST API client. Query bioactive molecules, targets,"
" activities, and more. All output is written to --output file."
)
)
subparsers = parser.add_subparsers(
dest="command", help="API endpoint to query"
)
for cmd_name in sorted(ENDPOINT_MAP.keys()):
api_name = ENDPOINT_MAP[cmd_name]
searchable = " (searchable)" if api_name in SEARCHABLE_ENDPOINTS else ""
sp = subparsers.add_parser(
cmd_name, help=f"Query {api_name} endpoint{searchable}"
)
_add_common_args(sp)
if cmd_name == "activity":
sp.add_argument(
"--normalize",
action="store_true",
help="Normalize bioactivity values to nM",
)
if cmd_name == "molecule":
sp.add_argument(
"--dl_format",
type=str,
choices=["sdf", "mol"],
help="Download molecule structure file (SDF or MOL)",
)
sp.set_defaults(func=cmd_generic)
sp_status = subparsers.add_parser("status", help="Check ChEMBL API status")
sp_status.add_argument(
"--output",
type=str,
required=True,
help="Output JSON file path (required)",
)
sp_status.set_defaults(func=cmd_status)
sp_sim = subparsers.add_parser(
"similarity", help="Server-side similarity search by SMILES"
)
sp_sim.add_argument("--smiles", type=str, required=True, help="SMILES string")
sp_sim.add_argument(
"--similarity",
type=int,
required=True,
help="Similarity threshold (0-100)",
)
sp_sim.add_argument(
"--limit",
type=int,
default=5,
help="Max results (default: 5)",
)
sp_sim.add_argument(
"--offset",
type=int,
default=None,
help="Pagination offset",
)
sp_sim.add_argument(
"--output",
type=str,
required=True,
help="Output JSON file path (required)",
)
sp_sim.set_defaults(func=cmd_similarity)
sp_sub = subparsers.add_parser(
"substructure", help="Server-side substructure search by SMILES"
)
sp_sub.add_argument("--smiles", type=str, required=True, help="SMILES string")
sp_sub.add_argument(
"--limit",
type=int,
default=5,
help="Max results (default: 5)",
)
sp_sub.add_argument(
"--offset",
type=int,
default=None,
help="Pagination offset",
)
sp_sub.add_argument(
"--output",
type=str,
required=True,
help="Output JSON file path (required)",
)
sp_sub.set_defaults(func=cmd_substructure)
sp_img = subparsers.add_parser(
"image", help="Download compound image (SVG by default)"
)
sp_img.add_argument(
"--id",
type=str,
required=True,
help="ChEMBL ID or InChI Key",
)
sp_img.add_argument(
"--output",
type=str,
required=True,
help="Output file path",
)
sp_img.add_argument(
"--dimensions",
type=int,
help="Image size in pixels (max 500, default 500)",
)
sp_img.add_argument(
"--engine",
type=str,
default=None,
help="Rendering engine (default: rdkit)",
)
sp_img.add_argument(
"--img_format",
type=str,
choices=["svg", "png"],
default=None,
help="Image format: svg (default) or png",
)
sp_img.set_defaults(func=cmd_image)
return parser
if __name__ == "__main__":
main_parser = build_parser()
main_args = main_parser.parse_args()
if not main_args.command:
main_parser.print_help()
sys.exit(1)
if getattr(main_args, "dl_format", None):
cmd_molecule_download(main_args)
else:
main_args.func(main_args)
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Low risk
- Scripts that your agent may run
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/chembl_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Google DeepMind or Google DeepMind.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download chembl-database.zip