Dataset Overview
Structured Pharmaceutical Datasetis provided byChangsha Langhui Information Technology Co., Ltd.China's drug knowledge database, built on the DataAssetsAPI platform. A collection of 5 million+ drug-structured information from NMPA drug registration databases, Chinese Pharmacopoeia, drug package inserts and real-world drug use data covers the entire life cycle of medicines – from registration approval, production flows to clinical applications and pharmacovigilance, supporting drug research and development AI, clinical rational drug use decisions and drug safety monitoring.
| Dataset Name | Structured Pharmaceutical Dataset |
| Total Data Volume | 5 million + Drug record |
| Type of drug | Western (chemical/biological), Chinese, and ethnic medicines — all covered |
| ATC Classification | Coverage of the ATC level 5 classification system (anatomical-therapeutic-pharmacological-chemical) |
| Source Data | NMPA Registered Database of Medicines, Chinese Pharmacopoeia (ChP), Drug Package Inserts, Real-World Use Data |
| Prescription Type | Prescription drugs (Rx), non-prescription drugs (OTC-A/B), and narcotic/psychotropic controlled categories are fully covered |
| Data Type | Structured JSON + Drug Package Insert Text + Clinical Trial Index |
| Field Coverage | 15+ core dimensions, covering generic/trade names/agent types/specifications/ATC classification/indications/adverse events/activities/interactions/toxicity/pharmacological classification/approval numbers/manufacturer/drug description text/clinical trial phase |
| Format Output | Structured JSON + CSV Export + API Access |
Dataset fields
The data set is organized according to the NMPA registration and ATC code systems, covering the entire drug registration, clinical and safety links.
| Dimension Category | Field Count | Typical Fields | Value Tier |
|---|---|---|---|
| Drug identity | 8 | Drug generic name (INN/Chinese generic name), trade name/brand name, approval number (national generic), registration classification (chemicals categories 1-5/biological products/Chinese medicine), NMPA registration status (approved/re-registered/cancelled) | Basic Identifier |
| ATCClassification | 6 | ATC Code Level 1-5: Anatomical Main Group, Therapeutic Subgroup, Pharmacological Subgroup, Chemical Subgroup, Chemical Substance Name | Classification System |
| Dosage Form and Specification | 8 | Agent type (tablets/capsules/injections/oral liquids, etc.), specification (content/concentration), route of administration (oral/inhalation/injection/topical, etc.), packaging materials, storage conditions, shelf life | Pharmacological Properties |
| Indications | 6 | Indications (NMPA approved), evidence (documentary support), indication ICD-11 code, applicable population groups (adult/child/elderly), contraindications, precautions | Clinical Indications |
| Usage | 10 | Dosage (routine adult/child/elderly/renal function adjustment), frequency of administration, maximum daily dose, treatment course, timing (before/after meals/fasting), rate of administration (injection), solvent requirements | Drug Instructions |
| Security | 12 | Adverse reactions (common/uncommon/frequent), incidence of adverse effects, serious adverse events (SAE), drug-drug interactions (DDI), DDI severity/mechanism, contraindicated drug combinations, special population use (pregnancy/lactation/children/elderly), drug overdose | Security alert. |
| Pharmacology | 8 | Pharmacological classification (NMPA/ATC), mechanism of action (MoA), target/receptor, pharmacokinetic parameters (Tmax/Cmax/AUC/t1/2/protein binding ratio), summary of toxicological studies | Pharmacological Basis |
| Notes text | 6 | Full text of the drug package insert, revision date, version number of the insert, number of pages of the insert, structured core field extraction confidence, index of the insert to the original version of the insert OCR | Full-text assets |
| clinical trials | 8 | Clinical trial registration number (NMPA/CDE), trial phase (I/II/III/IV/BE), trial status, indication, applicant, primary endpoint, number of enrolled cases, trial completion date | Research and Development Evidence |
| Manufacturer | 6 | Manufacturer name, manufacturing address, GMP certificate number, enterprise type (originator/generic/import), marketing authorization holder (MAH), contact information (redacted) | Traceability |
| Metadata | 6 | Data inclusion date, last update date, data source, quality rating, completeness score, and version number | Quality Management |
De-identified JSON Data Sample
The following is a structured JSON sample of de-identified drugs, with the core fields fully maintained.
{ "drug_id": "DRUG-2025-A8T3V2S6R1", "generic_name": "Atorvastatin calcium tablets", "generic_name_en": "Atorvastatin Calcium Tablets", "brand_names": ["Lipitor","Lipitor"], "approval_number": "NMPA approval HXXXXXXXX", "drug_category": "chemical drug", "registration_class": "chemical drug4sample（generic）", "rx_type": "RX（prescription drug）", "atc": { "code": "C10AA05", "level1": "C cardiovascular system", "level2": "C10 lipid-modifying agent", "level3": "C10A lipid-modifying agent，sample", "level4": "C10AA HMG-CoACN", "level5": "C10AA05 atorvastatin" }, "dosage_form": "sample value", "strength": "20mg", "route": "oral", "indications": { "approved": ["sample","mixed hyperlipidemia","sample"], "icd_codes": ["5C80.00","5C80.2"] }, "administration": { "adult_usual": "10-20mg，daily1sample", "max_daily": "80mg", "timing": "sample，sample" }, "adverse_effects": { "common": ["sample","sample","diarrhea","sample","sample","ALTelevated"], "serious": ["sample","sample","sample"] }, "interactions": [ {"drug": "sample value","severity":"contraindications","mechanism":"OATP1B1sample"}, {"drug": "sample value","severity":"sample(≤20mg)","mechanism":"CYP3A4sample"} ], "contraindications": ["Hypersensitivity","CNelevatedCN3sample","Hypersensitivity"], "pharmacology": { "class": "HMG-CoACN", "mechanism": "CNHMG-CoACN，sample", "pk": {"tmax_h":1.5,"half_life_h":14,"protein_binding":"≥98%"} }, "manufacturer": {"name": "Technique name","mah": "sample value","gmp":"NMPA approval GMPXXXXXX"}, "package_insert_update": "2024-06-15", "version": "2025Q2", "quality_score": 99.2
}AI Application Scenarios
I. Decision-making on rational use of medicines
5 million+ drug indications, usage and interaction data trained in the rational use of the AAI model, resulting in prescription clearance, superscript drug identification and individualisation of drug regimen recommendations.
Pharmacovigilance signal detection
Use adverse reaction, drug interaction, and serious adverse event data to train safety-signal detection models for post-market pharmacovigilance, causality assessment, and drug re-evaluation.
Drug development intelligence
ATC Classification + Clinical Trial + Approval of the Documented Data Training Drug Development Competitive Intelligence Model to support pipeline analysis, target tracking and market pattern analysis.
Structured information extraction from drug package inserts
The full text of the drug instruction document + core field extraction of the training NLP model, autostructure the drug instruction document and extract key information.
Decision support for reimbursement negotiations
Combine drug approval identifiers, indications, clinical trial records, and ATC classifications to train decision-support models for reimbursement negotiations, drug value assessment, and pricing analysis.
Antibiotic Management (AMS)
Antibiotic + antibacterial spectrometry + microbial sensitivity + DDI data to train antibacterial drug management models to support empirical drug referral and resistance monitoring.
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