How do I install the Molecular Targets Query skill?

Run npx skillmds@latest add gabrielmoreira/molecular-targets-query in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Molecular Targets Query safe to use?

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Which AI agents work with Molecular Targets Query?

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Is Molecular Targets Query free to use?

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Who published Molecular Targets Query?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Molecular Targets Query by gabrielmoreira

Name: Molecular Targets Query
Rating: 4.666666666666667 (3 reviews)
Author: gabrielmoreira

Molecular Targets Query

Query the NCI CCDI Molecular Targets Platform (pediatric oncology) for targets (genes), diseases, drugs, and target-disease associations via its public GraphQL API. Auto-detects entity type from input string.

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

Molecular Targets Platform Query Skill

Search the CCDI Molecular Targets Platform — an NCI-supported Open Targets instance focused on preclinical pediatric oncology data. Includes FDA Pediatric Molecular Target Lists and additional pediatric cancer datasets.

Entity Auto-Detection

Input Pattern	Detected As	Query Used
`ENSG00000141510`	Target (Ensembl gene)	`target(ensemblId)`
`EFO_\d+`, `MONDO_\d+`, `Orphanet_\d+`, `HP_\d+`, `DOID_\d+`	Disease / Phenotype	`disease(efoId)`
`CHEMBL\d+`	Drug / molecule	`drug(chemblId)`
anything else	Free text	`search(queryString)`

API

Function	Input	Returns
`search(entity, size=10)`	single entity string	`dict` (GraphQL data) or `None`
`search_batch(entities)`	list of strings	`dict[str, dict\|None]` — uses batch GraphQL where possible
`get_associations(entity, size=10)`	Ensembl ID or EFO/MONDO ID	associated diseases (for target) or targets (for disease)
`summarize(data, entity)`	result dict + label	compact LLM-readable text
`to_json(data)`	result dict	`list[dict]` for pipeline output

Helper

Function	Purpose
`detect_entity_type(entity)`	returns `'target'`, `'disease'`, `'drug'`, or `'search'`

Usage

See if __name__ == "__main__" block in 16_MolecularTargets.py for runnable examples covering:

Free-text search ("neuroblastoma")
Target lookup by Ensembl ID ("ENSG00000141510" → TP53)
Disease lookup by ontology ID ("MONDO_0005072")
Drug lookup by ChEMBL ID ("CHEMBL941" → imatinib)
Target → disease associations
Disease → target associations
Batch search (mixed entity types)
JSON pipeline output

Data Source

Platform: NCI CCDI Molecular Targets Platform
URL: https://moleculartargets.ccdi.cancer.gov
API endpoint: https://moleculartargets.ccdi.cancer.gov/api/v4/graphql
Method: HTTP POST, Content-Type: application/json, no auth required
Upstream schema: Open Targets Platform GraphQL v4
Focus: Pediatric oncology — FDA Pediatric Molecular Target Lists, preclinical data

Notes

The API is optimised for single-entity queries. For bulk analyses consider Open Targets data downloads.
Batch functions (search_batch) use the plural GraphQL fields (targets, diseases, drugs) to reduce round-trips where possible; free-text queries fall back to one-at-a-time.
get_associations() returns scored target↔disease links ranked by overall evidence score (0–1). Use size to control how many rows are returned.
Returns None / [] on HTTP errors — callers handle no-results gracefully.

---name: molecular-targets-querydescription: Query the NCI CCDI Molecular Targets Platform (pediatric oncology) for targets (genes), diseases, drugs, and target-disease associations via its public GraphQL API. Auto-detects entity type from input string.--- # Molecular Targets Platform Query Skill Search the CCDI Molecular Targets Platform — an NCI-supported Open Targets instance focused on **preclinical pediatric oncology** data. Includes FDA Pediatric Molecular Target Lists and additional pediatric cancer datasets. ## Entity Auto-Detection | Input Pattern | Detected As | Query Used | |---|---|---| | `ENSG00000141510` | Target (Ensembl gene) | `target(ensemblId)` | | `EFO_\d+`, `MONDO_\d+`, `Orphanet_\d+`, `HP_\d+`, `DOID_\d+` | Disease / Phenotype | `disease(efoId)` | | `CHEMBL\d+` | Drug / molecule | `drug(chemblId)` | | anything else | Free text | `search(queryString)` | ## API | Function | Input | Returns | |---|---|---| | `search(entity, size=10)` | single entity string | `dict` (GraphQL data) or `None` | | `search_batch(entities)` | list of strings | `dict[str, dict\|None]` — uses batch GraphQL where possible | | `get_associations(entity, size=10)` | Ensembl ID or EFO/MONDO ID | associated diseases (for target) or targets (for disease) | | `summarize(data, entity)` | result dict + label | compact LLM-readable text | | `to_json(data)` | result dict | `list[dict]` for pipeline output | ### Helper | Function | Purpose | |---|---| | `detect_entity_type(entity)` | returns `'target'`, `'disease'`, `'drug'`, or `'search'` | ## Usage See `if __name__ == "__main__"` block in `16_MolecularTargets.py` for runnable examples covering: 1. Free-text search (`"neuroblastoma"`) 2. Target lookup by Ensembl ID (`"ENSG00000141510"` → TP53) 3. Disease lookup by ontology ID (`"MONDO_0005072"`) 4. Drug lookup by ChEMBL ID (`"CHEMBL941"` → imatinib) 5. Target → disease associations 6. Disease → target associations 7. Batch search (mixed entity types) 8. JSON pipeline output ## Data Source - **Platform**: NCI CCDI Molecular Targets Platform - **URL**: <https://moleculartargets.ccdi.cancer.gov> - **API endpoint**: `https://moleculartargets.ccdi.cancer.gov/api/v4/graphql` - **Method**: HTTP POST, `Content-Type: application/json`, no auth required - **Upstream schema**: Open Targets Platform GraphQL v4 - **Focus**: Pediatric oncology — FDA Pediatric Molecular Target Lists, preclinical data ## Notes - The API is optimised for single-entity queries. For bulk analyses consider Open Targets data downloads. - Batch functions (`search_batch`) use the plural GraphQL fields (`targets`, `diseases`, `drugs`) to reduce round-trips where possible; free-text queries fall back to one-at-a-time. - `get_associations()` returns scored target↔disease links ranked by overall evidence score (0–1). Use `size` to control how many rows are returned. - Returns `None` / `[]` on HTTP errors — callers handle no-results gracefully.

Frequently asked questions