Traditional pharmaceutical databases offer rigid, pre-defined fields. Entity Enricher lets you define exactly what data you need -- from molecular properties and clinical trial phases to regulatory filings and safety profiles -- and enriches it using multiple AI models for cross-validated accuracy.
Pharmaceutical entities are uniquely complex. A single compound may have dozens of brand names across different markets, multiple ongoing clinical trials at various phases, evolving regulatory statuses across jurisdictions, and a safety profile that changes with post-market surveillance data. Static databases quickly become outdated, and manual curation does not scale.
With Entity Enricher, you define a schema that captures exactly the fields your pipeline needs. The multi-model enrichment engine queries multiple LLMs simultaneously, and the fusion layer resolves discrepancies -- for example, when one model reports an FDA approval date that differs from another, the arbitration system identifies the most reliable answer with full reasoning.
Below is a sample schema for enriching pharmaceutical compounds. You can generate schemas like this automatically from sample JSON using AI schema generation, or build them visually in the schema editor.
{
"name": "PharmaceuticalCompound",
"properties": {
"compound_name": { "type": "string", "is_key": true },
"inn_name": { "type": "string" },
"cas_number": { "type": "string" },
"molecular_formula": { "type": "string" },
"molecular_weight": { "type": "number" },
"mechanism_of_action": { "type": "string" },
"therapeutic_area": { "type": "string" },
"regulatory_status": {
"type": "object",
"properties": {
"fda_approval": { "type": "string" },
"ema_approval": { "type": "string" },
"first_approval_date": { "type": "string" }
}
},
"clinical_trials": {
"type": "array",
"items": {
"type": "object",
"properties": {
"phase": { "type": "string" },
"indication": { "type": "string" },
"status": { "type": "string" },
"nct_id": { "type": "string" }
}
}
},
"safety_profile": {
"type": "object",
"properties": {
"black_box_warning": { "type": "boolean" },
"common_adverse_effects": { "type": "array" },
"contraindications": { "type": "array" }
}
}
}
}Entity Enricher splits your schema into expertise domains, running parallel LLM calls for each domain. This produces deeper, more specialized results than a single monolithic prompt.
| Field | Expertise | Description |
|---|---|---|
| compound_name | General | Brand and generic names across markets |
| inn_name | General | International Nonproprietary Name (WHO designation) |
| cas_number | Chemistry | Chemical Abstracts Service registry number |
| molecular_formula | Chemistry | Molecular formula and structural class |
| mechanism_of_action | Pharmacology | Drug target and pharmacodynamic mechanism |
| regulatory_status | Regulatory | FDA/EMA approval status and dates |
| clinical_trials | Clinical | Active and completed trial phases, indications, NCT IDs |
| safety_profile | Safety | Black box warnings, adverse effects, contraindications |
Paste a sample compound JSON or describe your fields. AI generates a typed schema with expertise domains for regulatory, clinical, chemistry, and safety data.
Provide compound names, CAS numbers, or partial data. Use batch mode to process entire drug libraries at once.
Multiple LLMs enrich each compound in parallel. Pre-flight classification verifies the entity type before enrichment begins.
Conflicts between models are detected and resolved. Export enriched data as JSON or Excel for integration into your pharmaceutical pipeline.
Track clinical trial progression across your competitive landscape. Enrich compound names with trial phases, endpoints, and timelines.
Monitor FDA and EMA approval statuses, REMS requirements, and post-market commitments across jurisdictions.
Aggregate adverse event profiles, black box warnings, and contraindications for pharmacovigilance workflows.
Enrich compounds with patent expiration dates, exclusivity periods, and generic entry timelines.
Define your compound schema, connect your LLM provider keys, and get structured pharmaceutical intelligence in minutes. No pre-built field limitations.
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