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Opentargets Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Open Targets Database skill provides a structured interface to the Open Targets Platform GraphQL API. It enables researchers to retrieve aggregated evidence from genetics, pathways, animal models, and clinical trials. By using the provided Python wrapper, users can perform complex queries to rank target-disease associations, identify druggable genes, and explore clinical trial data while adhering to API best practices and fair-use policies.

What it does

  • Fetches target-disease associations and ranks them based on multi-modal evidence.
  • Retrieves druggability, tractability, and clinical safety data for specific gene targets.
  • Identifies drugs and clinical candidates associated with specific diseases.
  • Performs Locus-to-Gene (L2G) predictions to identify causal genes at genomic loci.
  • Searches for disease metadata using standard identifiers like MONDO or EFO.
  • Executes custom GraphQL queries for advanced data retrieval beyond built-in commands.

When to use it

  • When identifying potential therapeutic targets for a specific disease.
  • When exploring the druggability of a gene or protein.
  • When investigating the genetic evidence supporting a target-disease association.
  • When looking for clinical trial information for drugs related to a specific condition.
  • When performing fine-mapping analysis using credible sets from GWAS studies.

Advantages

  • Simplifies complex GraphQL API interactions into easy-to-use command-line subcommands.
  • Includes built-in retry logic and fair-use rate limiting.
  • Automatically handles response truncation to manage context window usage.
  • Provides standardized tools for genomic range searches and L2G analysis.
  • Ensures consistent output formatting via JSON files.

Usage examples

“Find all target genes associated with Rheumatoid Arthritis.”
The agent uses the get-associated-targets command with the appropriate disease ID to return a list of associated genes and their evidence scores.
“What is the druggability status of the gene ENSG00000169083?”
The agent executes the get-target-druggability command to retrieve tractability data and clinical trial safety information for the specified Ensembl ID.
“Find all drugs in Phase III or higher for a specific disease.”
The agent uses the get-disease-drugs command with the --min-stage PHASE_3 flag to filter and return relevant clinical candidates.
“Identify the most likely causal gene for a specific variant.”
The agent runs the get-l2g command for the given variant ID to retrieve Locus-to-Gene predictions.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/opentargets-database/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip opentargets-database.zip -d .agents/skills

Documentation by Google DeepMind

Open Targets Database Skill

Overview

This skill provides access to the Open Targets Platform GraphQL API. It aggregates multi-modal evidence from genetics (GWAS/eQTL), pathways, animal models, and clinical trials to rank target-disease associations and identify druggable genes.

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. User Notification: If .licenses/opentargets_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://platform-docs.opentargets.org/licence, then (2) create the file recording the notification text and timestamp.

Core Rules

  • Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce fair use and implement retry logic.

  • Output Flag: The --output flag is always required as output can be very large. Use jq or write your own code to process this JSON file.

  • Notification: If this skill is used, ensure this is mentioned in the output.

Quick Reference

Always use the provided Python script scripts/query_opentargets.py to quickly query the database. It handles API communication, retries, formatting, and automatically truncates overly large responses. NEVER write your own curl or similar requests.

Usage:

uv run scripts/query_opentargets.py --output /tmp/opentargets_results.json [OPTIONS] COMMAND [ARGS]...

Common Options:

  • --output PATH: Required. Path to write the JSON output file.

  • --limit N: Limit the number of items returned in arrays (default is 50). Use a smaller number like 10 when doing preliminary exploration.

  • --page-size N: Set the API pagination size (default is 200). Increase if you need more results (e.g., a study with many credible sets).

Available Commands:

  • get-gwas-studies disease_id: Fetches all GWAS studies associated with a specific disease ID (e.g. MONDO_0008383 for Rheumatoid Arthritis).

  • get-study-credible-sets study_id: Fetches all credible sets for a given study ID (e.g. FINNGEN_R12_RX_CROHN_2NDLINE). Returns confidence, finemapping method, variant, and p-value info.

  • get-qtl-credible-sets variant_id: Retrieves QTL credible sets for a specific variant ID (e.g. 19_44908822_C_T).

  • get-l2g variant_id [--study-id ID]: Returns Locus-to-Gene (L2G) predictions/scores for a locus to identify the most likely causal gene. Only variant_id is required; use --study-id to filter to a specific study. Accepts chr prefix (e.g. chr1_113834946_A_G).

  • get-target-druggability ensembl_id: Provides tractability data (small molecule, antibody, etc.) and clinical trial safety info for a gene/target.

  • get-associated-targets disease_id: Find all target genes associated with a specific disease ID (EFO or MONDO).

  • get-disease-drugs disease_id [--min-stage STAGE]: Find all drugs and clinical candidates associated with a disease. Use --min-stage to filter (e.g., PHASE_3 for Phase III or Approved).

  • get-associated-diseases ensembl_id: Find all diseases associated with a specific target Ensembl ID.

  • search-disease query_string: Search for a disease by name to find its ID and other metadata.

  • get-credible-sets-near-target ensembl_id [--window N]: Fetches credible sets for a target and filters them to those within a genomic window around the target. Useful for finding variants "nearby" a gene.

  • custom-query query [--variables '{}']: Run a raw GraphQL query for any other Open Targets data.

L2G Query Usage

The get-l2g command has two modes:

  • Variant only (get-l2g <variant_id>): Returns L2G predictions from all credible sets across all studies where that variant is the lead variant. This can return a large number of results (e.g., hundreds). Use this when the user wants a broad view of which gene is most likely causal at a locus, or when no specific study is mentioned.

  • Variant + study (get-l2g <variant_id> --study-id <study_id>): Returns L2G predictions only for credible sets from that specific study. Use this when the user asks about a specific GWAS study or when you need to narrow down the results.

Incomplete results warning: The variant-only mode can return hundreds of credible sets. The default --page-size is 200, so if the API reports a count higher than the number of rows returned, you are seeing incomplete results. Always compare count to the actual number of rows. If they differ, either increase --page-size or inform the user that only a subset was retrieved.

Querying by Region

To find studies with variants "nearby" a gene, use get-credible-sets-near-target, which improves upon the base API by performing a flexible search based on genomic position: uv run scripts/query_opentargets.py --output /tmp/results.json get-credible-sets-near-target ENSG00000156515 --window 500000

Note that the Open Targets GraphQL schema includes a regions parameter for credibleSets, however it performs an exact match against pre-computed region strings (e.g., chr10:68769984-69903496) and there is some missing data. Use get-credible-sets-near-target as it allows a genomic range overlap search.

This fetches credible sets associated with the target and filters them in Python based on the variant's genomic position.

Advanced GraphQL Queries

If you need to query endpoints or fields not exposed by the built-in subcommands, use the custom-query subcommand.

Before writing a custom query: Read the reference documentation to understand the API schema, types, and see example queries. See references/OpenTargets_GraphQL_Guide.md for full schema details, endpoints, and examples.

Example: Finding drugs for a disease

uv run scripts/query_opentargets.py custom-query \
  query drugsForDisease($id: String!) {
    disease(efoId: $id) {
      name
      drugAndClinicalCandidates {
        count
        rows {
          maxClinicalStage
          drug {
            id
            name
          }
        }
      }
    }
  }' \
--variables '{"id": "EFO_1001006"}'
--output '/tmp/opentargets_result.json'

Confidence Star Ratings

The Open Targets Platform assigns a confidence level to each credible set based on the fine-mapping method and quality checks. These correspond to star ratings displayed in the platform UI:

StarsConfidence String (API value)
★★★★ (4 stars)SuSiE fine-mapped credible set with in-sample LD
★★★ (3 stars)SuSiE fine-mapped credible set with out-of-sample LD
★★ (2 stars)`PICS fine-mapped credible set extracted from summary
: : statistics` :
★ (1 star)PICS fine-mapped credible set based on reported top hit
NoneUnknown confidence

When users ask about "N-star confidence", match their request to the corresponding string in the confidence field of the API response.

Tips and Common Mistakes

  • ID Formats:

    • Disease IDs are typically MONDO IDs (e.g. MONDO_0008383 for Rheumatoid Arthritis) or EFO IDs (e.g. EFO_0009460). Use the search-disease command to find the correct ID.

    • Target IDs must be Ensembl IDs (e.g. ENSG00000169083), not HGNC symbols. If you only have a gene symbol, you may need to map it first using a custom GraphQL search query.

    • Variant IDs are formatted as chromosome_position_ref_alt (e.g., 1_154426264_C_T). A chr prefix (e.g. chr1_154426264_C_T) is automatically stripped by the tool.

    • Study IDs can be GWAS Catalog IDs (e.g. GCST90204201) or project-specific IDs (e.g. FINNGEN_R12_RX_CROHN_2NDLINE).

  • Truncation: The tool truncates arrays longer than --limit to protect the context window. If you see "_truncated", you can run the query again with a higher limit if you specifically need more data, but be cautious with large limit values. Always use the --output flag to save the result to a file and avoid terminal output truncation.

  • Pagination and incomplete results: The --page-size option (default: 200) controls how many items are fetched from the API. Always check the count field in the response and compare it to the number of rows actually returned. If count > number of rows, you have incomplete data — either increase --page-size to fetch more, or inform the user that only a partial result set was returned. This is especially important for get-l2g without --study-id, which can return hundreds of credible sets.

Files

scripts/query_opentargets.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.

"""Queries the Open Targets Platform GraphQL API.

This script provides a command-line interface to query various endpoints of the
Open Targets GraphQL API, including GWAS studies, QTL credible sets, L2G
predictions, target druggability, and disease/target associations.
"""

# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "polite-http",
# ]
# ///

import argparse
import json
import re
import sys
from typing import Any

from polite_http import http_client

BASE_URL = "https://api.platform.opentargets.org/api/v4/graphql"
_CLIENT = http_client.HttpClient(BASE_URL, qps=1.0)

CLINICAL_STAGES = ["PHASE_0", "PHASE_1", "PHASE_2", "PHASE_3", "APPROVAL"]
STAGE_ORDER = {stage: i for i, stage in enumerate(CLINICAL_STAGES)}


def normalize_variant_id(variant_id: str) -> str:
  """Normalize variant ID by stripping 'chr' prefix if present."""
  return re.sub(r"^chr", "", variant_id, flags=re.IGNORECASE)


def truncate_data(data: Any, limit: int = 50) -> Any:
  """Recursively truncates lists within a dictionary or list to a given limit.

  This function is designed to limit the size of potentially large lists within
  the JSON response from the OpenTargets API, making the output more manageable.
  Specifically handles the "rows" field within dictionaries that also have a
  "count" field, common in OpenTargets GraphQL responses.

  Args:
    data: The data structure (dict or list) to potentially truncate.
    limit: The maximum number of items to keep in any list.

  Returns:
    The truncated data structure.
  """
  if isinstance(data, list):
    if len(data) > limit:
      result = [truncate_data(item, limit) for item in data[:limit]]
      result.append({"_truncated": f"(Showing {limit} of {len(data)} items)"})
      return result
    return [truncate_data(item, limit) for item in data]
  elif isinstance(data, dict):
    result = {}
    for k, v in data.items():
      if k == "rows" and isinstance(v, list) and "count" in data:
        # specific to Open Targets GraphQL
        count = data.get("count", len(v))
        if len(v) > limit:
          result[k] = [truncate_data(item, limit) for item in v[:limit]]
          result[k].append({"_truncated": f"(Showing {limit} of {count} rows)"})
        else:
          result[k] = [truncate_data(item, limit) for item in v]
      else:
        result[k] = truncate_data(v, limit)
    return result
  else:
    return data


def execute_query(
    query: str,
    variables: dict[str, Any],
    output_file: str,
    limit: int = 50,
    page_size: int = 200,
):
  """Executes a GraphQL query against the Open Targets API.

  This function sends a POST request to the Open Targets GraphQL API. It
  automatically injects pagination variables if the query supports it and
  pagination is not already specified. The response is checked for errors,
  truncated to a manageable size, and then written to the specified output file
  as JSON.

  Args:
    query: The GraphQL query string.
    variables: A dictionary of variables to pass with the query.
    output_file: The path to the file where the JSON output will be written.
    limit: The maximum number of items to keep in lists within the response
      (used by truncate_data). Defaults to 50.
    page_size: The size of each page for API pagination. If provided and the
      query supports "$page", pagination variables will be added. Defaults to
      200.
  """
  # Inject pagination into variables the query supports it and not already set
  if "page" not in variables and page_size and "$page" in query:
    variables["page"] = {"index": 0, "size": page_size}

  data = _CLIENT.fetch_json(
      BASE_URL,
      method="POST",
      json_body={"query": query, "variables": variables},
  )

  if "errors" in data:
    print(
        f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
        file=sys.stderr,
    )
    sys.exit(1)

  truncated_data = truncate_data(data.get("data", {}), limit)
  with open(output_file, "w", encoding="utf-8") as f:
    f.write(json.dumps(truncated_data, indent=2))


def main():
  parser = argparse.ArgumentParser(
      description="Query Open Targets Platform GraphQL API"
  )
  parser.add_argument(
      "--limit",
      type=int,
      default=50,
      help="Limit list sizes in response (default: 50)",
  )
  parser.add_argument(
      "--page-size",
      type=int,
      default=200,
      help=(
          "API pagination size (default: 200). Set higher if results are"
          " truncated."
      ),
  )
  parser.add_argument(
      "--output",
      required=True,
      help="Path to write the JSON output file",
  )
  subparsers = parser.add_subparsers(dest="command", required=True)

  # get-gwas-studies
  p_gwas = subparsers.add_parser(
      "get-gwas-studies", help="Get GWAS studies for a specific disease EFO ID"
  )
  p_gwas.add_argument(
      "disease_id",
      help="Disease ID (EFO or MONDO, e.g., MONDO_0005011)",
  )

  # get-qtl-credible-sets
  p_qtl = subparsers.add_parser(
      "get-qtl-credible-sets",
      help="Get QTL credible sets for a specific variant",
  )
  p_qtl.add_argument("variant_id", help="Variant ID (e.g., 19_44908822_C_T)")

  # get-l2g
  p_l2g = subparsers.add_parser(
      "get-l2g",
      help=(
          "Get L2G prioritisation scores for a variant, optionally filtered by"
          " study"
      ),
  )
  p_l2g.add_argument(
      "variant_id",
      help="Lead variant ID (e.g., 1_113834946_A_G or chr1_113834946_A_G)",
  )
  p_l2g.add_argument(
      "--study-id",
      help="Optional study ID to filter results (e.g., GCST90204201)",
      default=None,
  )

  # get-target-druggability
  p_drug = subparsers.add_parser(
      "get-target-druggability",
      help="Get druggability and safety info for a target",
  )
  p_drug.add_argument(
      "ensembl_id", help="Target Ensembl ID (e.g., ENSG00000169083)"
  )

  # get-associated-targets
  p_assoc_tgt = subparsers.add_parser(
      "get-associated-targets",
      help="Get targets associated with a disease EFO ID",
  )
  p_assoc_tgt.add_argument(
      "disease_id",
      help="Disease ID (EFO or MONDO, e.g., MONDO_0005005)",
  )

  # get-associated-diseases
  p_assoc_dis = subparsers.add_parser(
      "get-associated-diseases",
      help="Get diseases associated with a target Ensembl ID",
  )
  p_assoc_dis.add_argument(
      "ensembl_id", help="Target Ensembl ID (e.g., ENSG00000127318)"
  )

  # get-study-credible-sets
  p_study_cs = subparsers.add_parser(
      "get-study-credible-sets",
      help="Get credible sets for a GWAS or other study by study ID",
  )
  p_study_cs.add_argument(
      "study_id",
      help="Study ID (e.g., FINNGEN_R12_RX_CROHN_2NDLINE or GCST90204201)",
  )
  # search-disease
  p_search_dis = subparsers.add_parser(
      "search-disease",
      help="Search for a disease by name to find its EFO ID and other metadata",
  )
  p_search_dis.add_argument(
      "query_string",
      help="Disease name or phenotype string to search for (e.g., 'asthma')",
  )

  # get-credible-sets-near-target
  p_near = subparsers.add_parser(
      "get-credible-sets-near-target",
      help="Get credible sets near target by filtering coordinates",
  )
  p_near.add_argument(
      "ensembl_id", help="Target Ensembl ID (e.g., ENSG00000156515)"
  )
  p_near.add_argument(
      "--window",
      type=int,
      default=500_000,
      help="Window size in bp around the target (default: 500000)",
  )

  # get-disease-drugs
  p_dis_drugs = subparsers.add_parser(
      "get-disease-drugs",
      help="Get drugs and clinical candidates for a disease",
  )
  p_dis_drugs.add_argument(
      "disease_id",
      help="Disease ID (EFO or MONDO, e.g., MONDO_0006823)",
  )
  p_dis_drugs.add_argument(
      "--min-stage",
      choices=CLINICAL_STAGES,
      help="Filter to drugs at or above this clinical stage for the disease",
  )

  # custom-query
  p_custom = subparsers.add_parser(
      "custom-query", help="Execute a custom GraphQL query"
  )
  p_custom.add_argument("query", help="GraphQL query string")
  p_custom.add_argument(
      "--variables", help="JSON string of variables", default="{}"
  )

  args = parser.parse_args()

  if args.command == "get-gwas-studies":
    query = """
        query getGWASStudies($efoId: String!) {
          studies(diseaseIds: [$efoId]) {
            count
            rows {
              id
              projectId
              traitFromSource
              publicationFirstAuthor
              publicationDate
              publicationJournal
              nSamples
              cohorts
              pubmedId
              ldPopulationStructure { ldPopulation relativeSampleSize }
            }
          }
        }
        """
    variables = {"efoId": args.disease_id}

  elif args.command == "get-qtl-credible-sets":
    query = """
        query getQTLCredibleSets($variantId: String!) {
          variant(variantId: $variantId) {
            id
            qtlCredibleSets: credibleSets(
              studyTypes: [scsqtl, sceqtl, scpqtl, sctuqtl, sqtl, eqtl, pqtl, tuqtl]
            ) {
              count
              rows {
                studyLocusId pValueMantissa pValueExponent beta finemappingMethod confidence
                variant { id chromosome }
                study {
                  id studyType condition
                  target { id approvedSymbol }
                  biosample { biosampleId biosampleName }
                }
                locus(variantIds: [$variantId]) {
                  rows { posteriorProbability }
                }
              }
            }
          }
        }
        """
    variables = {"variantId": normalize_variant_id(args.variant_id)}

  elif args.command == "get-l2g":
    variant_id = normalize_variant_id(args.variant_id)
    query = """
        query getL2G($variantIds: [String!], $studyIds: [String!], $page: Pagination) {
          credibleSets(variantIds: $variantIds, studyIds: $studyIds, page: $page) {
            count
            rows {
              studyLocusId
              confidence
              study { id studyType traitFromSource }
              variant { id }
              l2GPredictions {
                rows {
                  score
                  target { id approvedSymbol }
                }
              }
            }
          }
        }
        """
    variables = {"variantIds": [variant_id]}
    if args.study_id:
      variables["studyIds"] = [args.study_id]

  elif args.command == "get-target-druggability":
    query = """
        query getTargetAnnotation($ensemblId: String!) {
          target(ensemblId: $ensemblId) {
            id
            approvedSymbol
            tractability { modality label value }
            safetyLiabilities {
              event eventId
              biosamples { cellFormat cellLabel tissueLabel }
              effects { dosing direction }
              studies { name type description }
              datasource
            }
            geneticConstraint { constraintType exp obs score oe }
          }
        }
        """
    variables = {"ensemblId": args.ensembl_id}

  elif args.command == "get-associated-targets":
    query = """
        query getAssociatedTargets($efoId: String!) {
          disease(efoId: $efoId) {
            id
            name
            associatedTargets {
              count
              rows {
                target { id approvedSymbol }
                score
              }
            }
          }
        }
        """
    variables = {"efoId": args.disease_id}

  elif args.command == "get-associated-diseases":
    query = """
        query getAssociatedDiseases($ensemblId: String!) {
          target(ensemblId: $ensemblId) {
            id
            approvedSymbol
            associatedDiseases {
              count
              rows {
                disease { id name }
                datasourceScores { id score }
              }
            }
          }
        }
        """
    variables = {"ensemblId": args.ensembl_id}

  elif args.command == "get-study-credible-sets":
    query = """
        query getStudyCredibleSets($studyIds: [String!], $page: Pagination) {
          credibleSets(studyIds: $studyIds, page: $page) {
            count
            rows {
              studyLocusId
              confidence
              finemappingMethod
              pValueMantissa
              pValueExponent
              beta
              credibleSetIndex
              region
              variant { id chromosome position }
              study { id studyType traitFromSource }
            }
          }
        }
        """
    variables = {"studyIds": [args.study_id]}

  elif args.command == "search-disease":
    query = """
        query searchDisease($queryString: String!, $page: Pagination) {
          search(queryString: $queryString, entityNames: ["disease"], page: $page) {
            hits {
              id
              name
              description
              entity
            }
          }
        }
        """
    variables = {"queryString": args.query_string}

  elif args.command == "get-credible-sets-near-target":
    query = """
        query getTargetCredibleSets($ensemblId: String!, $page: Pagination) {
          target(ensemblId: $ensemblId) {
            approvedSymbol
            genomicLocation { chromosome start end }
            credibleSets(page: $page) {
              count
              rows {
                studyLocusId
                confidence
                region
                chromosome
                position
                variant { id chromosome position }
                study { id studyType traitFromSource }
              }
            }
          }
        }
        """
    variables = {"ensemblId": args.ensembl_id}
    if "page" not in variables and args.page_size and "$page" in query:
      variables["page"] = {"index": 0, "size": args.page_size}

    data = _CLIENT.fetch_json(
        BASE_URL,
        method="POST",
        json_body={"query": query, "variables": variables},
    )

    if "errors" in data:
      print(
          f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
          file=sys.stderr,
      )
      sys.exit(1)

    target_data = data.get("data", {}).get("target", {})
    if not target_data:
      print("Error: Target not found", file=sys.stderr)
      sys.exit(1)

    loc = target_data.get("genomicLocation", {})
    target_chrom = loc.get("chromosome")
    t_start = loc.get("start")
    t_end = loc.get("end")

    if t_start is None or t_end is None:
      print(
          "Warning: Target location not found, cannot filter by region.",
          file=sys.stderr,
      )
      min_pos = 0
      max_pos = sys.maxsize
    else:
      window = args.window
      min_pos = max(0, t_start - window)
      max_pos = t_end + window

    cs_data = target_data.get("credibleSets", {})
    rows = cs_data.get("rows", [])

    filtered_rows = []
    for row in rows:
      v_pos = row.get("position") or row.get("variant", {}).get("position")
      v_chrom = row.get("chromosome") or row.get("variant", {}).get(
          "chromosome"
      )
      if (
          v_pos is not None
          and v_chrom == target_chrom
          and min_pos <= v_pos <= max_pos
      ):
        filtered_rows.append(row)

    # Update data structure
    cs_data["total_count"] = cs_data.get("count")
    cs_data["rows"] = filtered_rows
    cs_data["filtered_count"] = len(filtered_rows)

    truncated_data = truncate_data(data.get("data", {}), args.limit)
    with open(args.output, "w", encoding="utf-8") as f:
      f.write(json.dumps(truncated_data, indent=2))

    sys.exit(0)

  elif args.command == "get-disease-drugs":
    query = """
        query getDiseaseDrugs($efoId: String!) {
          disease(efoId: $efoId) {
            id
            name
            drugAndClinicalCandidates {
              count
              rows {
                maxClinicalStage
                drug {
                  id
                  name
                  drugType
                  maximumClinicalStage
                }
              }
            }
          }
        }
        """
    variables = {"efoId": args.disease_id}

    data = _CLIENT.fetch_json(
        BASE_URL,
        method="POST",
        json_body={"query": query, "variables": variables},
    )
    if "errors" in data:
      print(
          f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
          file=sys.stderr,
      )
      sys.exit(1)

    disease_data = data.get("data", {}).get("disease")
    if disease_data and args.min_stage:
      candidates = disease_data.get("drugAndClinicalCandidates", {})
      rows = candidates.get("rows", [])
      filtered_rows = []
      min_stage_val = STAGE_ORDER.get(args.min_stage)
      for row in rows:
        stage = row.get("maxClinicalStage")
        stage_val = STAGE_ORDER.get(stage)
        if stage_val is not None and stage_val >= min_stage_val:
          filtered_rows.append(row)
      candidates["total_count"] = candidates.get("count")
      candidates["rows"] = filtered_rows
      candidates["filtered_count"] = len(filtered_rows)
      candidates["count"] = len(filtered_rows)

    truncated_data = truncate_data(data.get("data", {}), args.limit)
    with open(args.output, "w", encoding="utf-8") as f:
      f.write(json.dumps(truncated_data, indent=2))
    sys.exit(0)

  elif args.command == "custom-query":
    query = args.query
    try:
      variables = json.loads(args.variables)
    except json.JSONDecodeError:
      print("Error: --variables must be a valid JSON string", file=sys.stderr)
      sys.exit(1)
  else:
    available_commands = ", ".join(subparsers.choices.keys())
    print(
        f"Error: Unknown command: {args.command}. Available commands:"
        f" {available_commands}",
        file=sys.stderr,
    )
    sys.exit(1)

  execute_query(query, variables, args.output, args.limit, args.page_size)


if __name__ == "__main__":
  main()

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/opentargets_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download opentargets-database.zip