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search_chembl_molecule

Resolve a drug, compound, or molecule name or structure to its ChEMBL ID. Accepts names, brands, synonyms, InChIKey, InChI, and SMILES.

Instructions

Resolve a DRUG / COMPOUND / MOLECULE (by name or structure) to a ChEMBL ID.

✅ Use this tool for drug, compound, or molecule names (e.g., "sorafenib", "imatinib", "aspirin", "Gleevec"). ⚠️ For biological targets (proteins, receptors, enzymes, genes such as EGFR, BRCA1, TP53), use search_chembl_target instead.

Resolution path is auto-selected from the query shape:

• NAME / BRAND / SYNONYM → deterministic SPARQL, EXACT (case-insensitive) match on the molecule's skos:altLabel synonyms (which include brand and trade names — "Gleevec" → CHEMBL941 IMATINIB). Not fuzzy/substring: fix typos before calling. No relevance ranking to second-guess.

• InChIKey / InChI → deterministic SPARQL, EXACT (case-SENSITIVE) match on the RDF-stored identifier. These are canonical (toolkit-independent), so exact match is correct, e.g. "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" → CHEMBL25.

• SMILES → the ChEMBL REST chemistry engine (flexmatch), NOT exact match: a SMILES is written differently by each toolkit, so flexmatch normalizes the structure first, e.g. "CC(=O)Oc1ccccc1C(=O)O" → CHEMBL25.

Structure detection is conservative (multi-word input, or input without the "InChI=" prefix / InChIKey pattern / structural punctuation, is treated as a name), so a bare-chain SMILES like "CCO" is treated as a name.

The search string can be passed as any of: query (canonical), search, term, keyword, keywords, search_term, or name.

RETURNS a dict {'total_count', 'has_more', 'results'}. total_count is rows RETURNED (capped by limit), not the full match count; has_more is true if more exist beyond this page. Each result has 'chembl_id' (e.g. "CHEMBL25") and 'name' (rdfs:label, may be None for some structure hits). On endpoint/HTTP failure this tool does NOT raise — it returns a dict with a single 'error' key instead; CHECK FOR 'error' BEFORE READING 'results'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
termNo
limitNoMaximum number of results to return. Defaults to 20.
queryNoDrug/compound name, brand, synonym, or a structure string. Examples: "Aspirin", "Gleevec", "CC(=O)Oc1ccccc1C(=O)O", "BSYNRYMUTXBXSQ-UHFFFAOYSA-N".
searchNo
keywordNo
keywordsNo
search_termNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so description bears full burden. It discloses deterministic vs flexmatch matching, error handling (returns error key, not raises), return format with total_count meaning, and conservative structure detection. Fully transparent.

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

Conciseness4/5

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

Well-structured with bullet points and examples, front-loaded with main purpose. Slightly verbose in places (e.g., repeated error handling), but overall clear and organized.

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 tool complexity (multiple input types, different resolution methods, error handling) and presence of output schema (dict format described), the description is very complete. Explains return structure, error key, and limitations. Lacks detail on no-match case but still comprehensive.

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

Parameters4/5

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

Schema coverage is low (25%) but description compensates by explaining that multiple parameter names are accepted as aliases. It adds meaning beyond schema by describing resolution paths for different input types per parameter. Still, individual parameter semantics could be more detailed.

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?

The description clearly states the tool resolves a drug/compound/molecule to a ChEMBL ID, specifies input types (name, structure) with examples, and distinguishes from the sibling tool search_chembl_target for biological targets.

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 says when to use (drug/compound names) and when not to use (biological targets, pointing to sibling). Also details resolution paths for different input shapes, guiding correct usage.

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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