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search_chembl_target

Find biological drug targets (proteins, receptors, enzymes) by name, gene, or UniProt ID in ChEMBL. Returns target type, organism, and ChEMBL ID.

Instructions

Search for a biological TARGET (protein/receptor/enzyme) in ChEMBL.

⚠️ DO NOT use this tool to look up drugs, compounds, or molecules by name. For drug/compound/molecule names (e.g., "sorafenib", "imatinib", "aspirin"), use search_chembl_molecule instead.

This tool searches for biological entities that drugs act upon — proteins, protein complexes, nucleic acids, organisms, tissues, and cell lines. "Target" here means drug target, NOT "the thing I am looking up".

Only the search string and limit are supported. The search string can be passed as any of: query (canonical), search, term, keyword, keywords, search_term, or name.

Args: query (str): Search query string referring to a biological target. Examples: - Target name (e.g., "Thrombin", "EGFR", "Dopamine receptor") - Gene name (e.g., "BRCA1", "TP53") - UniProt accession (e.g., "P00734") - Organism name (e.g., "Homo sapiens") limit (int, optional): Maximum number of results to return. Defaults to 20.

Returns: dict: Dictionary containing: - 'total_count' (int): Total number of matching targets found - 'results' (list): List of target dictionaries, each containing: - 'chembl_id' (str): ChEMBL target identifier (e.g., "CHEMBL1824") - 'name' (str): Preferred target name - 'organism' (str): Organism name (e.g., "Homo sapiens") - 'type' (str): Target type (e.g., "SINGLE PROTEIN", "PROTEIN COMPLEX") - 'score' (float): Relevance score for the search query

Example: >>> results = await search_chembl_target("EGFR human", limit=5) >>> print(f"Found {results['total_count']} targets") >>> for target in results['results']: ... print(f"{target['chembl_id']}: {target['name']} ({target['organism']})")

Output:
Found 15 targets
CHEMBL203: Epidermal growth factor receptor (Homo sapiens)

Target Types: - SINGLE PROTEIN: Individual protein target - PROTEIN COMPLEX: Multi-protein complex - PROTEIN FAMILY: Group of related proteins - NUCLEIC-ACID: DNA/RNA targets - TISSUE: Tissue-level target - CELL-LINE: Cell line target - ORGANISM: Whole organism target

Raises: httpx.HTTPError: If the API request fails

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
limitNo
searchNo
termNo
keywordNo
keywordsNo
search_termNo
nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavior: only query and limit are used, many aliases for query, returns dictionaries with specific fields, and lists target types and possible errors.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections: warning, args, returns, example, target types, raises. Every sentence adds value, and critical warning is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the number of sibling tools and complexity of parameter aliases, the description is complete with example output, target type enumeration, and error handling information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond schema: explains which parameters are actually used, provides examples for query, and clarifies that other parameters are aliases. Schema has no descriptions, so description fully compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States clearly that it searches for biological targets in ChEMBL, with specific examples and direct distinction from search_chembl_molecule, which is for drugs/compounds.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly warns not to use for drugs/compounds and directs to the sibling tool search_chembl_molecule. Also explains the search can be by various names, gene names, etc.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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