hypokrates 0.7.1

Normalize and cross-reference global public health data for medical hypothesis generation

hypokrates

Democratizing pharmacovigilance through open public health data.

hypokrates is an open-source Python library that normalizes and cross-references 15 global pharmacovigilance and drug safety databases, exposing them via MCP so that any person with access to an LLM can generate medical hypotheses.

Hippocrates observed a few patients. Today we can observe millions. What's missing is the tool to ask better questions.

The name comes from the original Greek spelling of Hippocrates (Hippokrates) — who broke the model of his era by making medical knowledge open instead of guarded by temple priests. The "hypo" prefix also evokes "hypothesis". Public health data collected with public money, normalized and cross-referenced in an open library, so any doctor in the world can generate hypotheses that save lives.

The problem

Medical knowledge discovery has a bottleneck: hypothesis generation.

Tools like OpenEvidence and PubMed solve literature search — finding what has been studied. But they cannot find what has not been studied yet. Signals that exist in pharmacovigilance data (20M+ adverse event reports across 3 countries), molecular mechanism databases, and drug labels — but that no one has cross-referenced because the data lives in silos with different formats, vocabularies, and access patterns.

hypokrates cross-references FAERS + JADER + Canada Vigilance + PubMed + DailyMed + DrugBank + OpenTargets + ChEMBL + OnSIDES + PharmGKB + ClinicalTrials.gov + ANVISA — and returns a structured hypothesis with evidence level, in seconds.

Install

pip install hypokrates

# Optional extras
pip install hypokrates[trials]   # ClinicalTrials.gov (Cloudflare bypass via curl_cffi)
pip install hypokrates[mcp]      # MCP server (typer + mcp)

Quick start

from hypokrates.config import configure

# Optional: API keys raise rate limits
configure(
    openfda_api_key="your-key",     # 40 -> 240 req/min
    ncbi_api_key="your-key",        # 180 -> 600 req/min
    ncbi_email="[e-mail]",
)

Signal detection

Disproportionality analysis (PRR, ROR, IC, EBGM) for any drug-event pair:

from hypokrates.sync import stats

result = stats.signal("sugammadex", "bradycardia")
print(f"PRR: {result.prr.value:.2f}")
print(f"Signal: {result.signal_detected}")  # >= 2/3 measures significant

Hypothesis generation

Cross-reference FAERS signal + PubMed + up to 10 optional sources:

from hypokrates.sync import cross

result = cross.hypothesis(
    "sugammadex", "bradycardia",
    check_label=True,        # DailyMed FDA label
    check_trials=True,       # ClinicalTrials.gov
    check_chembl=True,       # ChEMBL mechanism
    check_opentargets=True,  # OpenTargets LRT score
    check_canada=True,       # Canada Vigilance cross-validation
    check_jader=True,        # JADER (Japan) cross-validation
)
print(result.classification)  # novel_hypothesis | emerging_signal | known_association | no_signal
print(result.summary)

Automated drug scanning

Scan top adverse events with parallel hypothesis generation:

from hypokrates.sync import scan

result = scan.scan_drug(
    "sugammadex",
    top_n=15,
    check_labels=True,
    check_chembl=True,
    primary_suspect_only=True,  # PS-only role filter (bulk data)
    check_direction=True,       # base PRR vs PS-only comparison
)
for item in result.items:
    print(f"#{item.rank} {item.event}: {item.classification.value} (score={item.score:.1f})")

Data sources

15 sources across 3 countries, all publicly accessible:

Source	Module	Coverage	Auth
OpenFDA/FAERS	faers	USA, 20M+ reports	Optional API key
FAERS Bulk	faers_bulk	USA, deduplicated	Local quarterly ZIPs
Canada Vigilance	canada	Canada, 738K+ reports	Local bulk download
JADER (PMDA)	jader	Japan, 1M+ reports	Local CSVs (free)
PubMed	pubmed	Global, 36M+ papers	Optional API key
DailyMed	dailymed	USA FDA labels	None
ClinicalTrials.gov	trials	Global	None (needs curl_cffi)
DrugBank	drugbank	Global	Local XML (free academic)
OpenTargets	opentargets	Global	None
ChEMBL	chembl	Global	None
OnSIDES	onsides	US/EU/UK/JP labels	Local CSVs (free)
PharmGKB	pharmgkb	Global pharmacogenomics	None
ANVISA	anvisa	Brazil drug registry	None (auto-download)
RxNorm	vocab	Drug name normalization	None
MeSH	vocab	Medical term mapping	None

Demographic stratification

FAERS Bulk and Canada Vigilance support filtering by sex and age group:

from hypokrates.faers_bulk.models import StrataFilter
from hypokrates.sync import faers_bulk

result = faers_bulk.bulk_signal(
    "rocuronium", "anaphylactic shock",
    strata=StrataFilter(sex="F", age_group="65+"),
)

Cross-country validation

The same drug-event pair checked across USA, Canada, and Japan:

from hypokrates.sync import stats, canada, jader

usa = stats.signal("rocuronium", "anaphylactic shock")
can = canada.canada_signal("rocuronium", "anaphylactic shock")
jpn = jader.jader_signal("rocuronium", "anaphylactic shock")

MCP Server

44 tools available for LLM integration via Model Context Protocol:

python -m hypokrates.mcp

Configure in Claude Desktop, Cursor, or any MCP client:

{
  "mcpServers": {
    "hypokrates": {
      "type": "stdio",
      "command": "python",
      "args": ["-m", "hypokrates.mcp"],
      "env": {
        "OPENFDA_API_KEY": "your-key",
        "NCBI_API_KEY": "your-key",
        "NCBI_EMAIL": "[e-mail]",
        "DRUGBANK_PATH": "/path/to/drugbank.xml",
        "ONSIDES_PATH": "/path/to/onsides/csvs/",
        "CANADA_BULK_PATH": "/path/to/canada/extracted/",
        "JADER_BULK_PATH": "/path/to/jader/csvs/",
        "FAERS_BULK_DIR": "/path/to/faers/quarterly/"
      }
    }
  }
}

Core tools: signal, hypothesis, scan_drug, compare_signals, compare_class

Source tools: adverse_events, top_events, drugs_by_event, search_papers, label_events, check_label, search_trials, drug_info, drug_interactions, drug_mechanism, drug_adverse_events, drug_safety_score, onsides_events, pgx_annotations, normalize_drug, map_to_mesh

Bulk tools: faers_bulk_signal, faers_bulk_load, canada_signal, canada_top_events, jader_signal, jader_top_events

Architecture

hypokrates/
├── faers/          # OpenFDA FAERS API (adverse events, co-suspect detection)
├── faers_bulk/     # FAERS quarterly ASCII (dedup, role filter, strata)
├── stats/          # Disproportionality measures (PRR, ROR, IC, EBGM)
├── cross/          # Hypothesis generation (signal + literature + enrichments)
├── scan/           # Automated drug scanning with scoring
├── evidence/       # Evidence blocks with provenance and limitations
├── pubmed/         # PubMed/NCBI E-utilities
├── vocab/          # RxNorm normalization + MedDRA synonym grouping
├── dailymed/       # FDA label parsing (SPL XML)
├── trials/         # ClinicalTrials.gov (curl_cffi for Cloudflare)
├── drugbank/       # DrugBank XML (mechanism, interactions, enzymes)
├── opentargets/    # OpenTargets Platform (GraphQL, LRT scores)
├── chembl/         # ChEMBL (mechanism, targets, metabolism)
├── onsides/        # OnSIDES international labels (NLP-extracted)
├── pharmgkb/       # PharmGKB pharmacogenomics (CPIC/DPWG guidelines)
├── canada/         # Canada Vigilance (cross-country validation)
├── jader/          # JADER/PMDA Japan (cross-country, JP→EN translation)
├── anvisa/         # ANVISA Brazil (drug registry, PT↔EN mapping)
├── cache/          # DuckDB HTTP cache (thread-safe singleton)
├── http/           # BaseClient with retry, rate limiting, auth
└── mcp/            # MCP server (44 tools)

Async-first with sync wrappers. DuckDB for cache and bulk stores. Pydantic 2 for all models. mypy strict. 1349 tests.

Who is this for

• The anesthesiologist who saw a pattern in patients and wants to know if it's real
• The researcher at a public university without a bioinformatics team
• The resident who disagrees with a protocol and wants data to support their case
• The doctor in rural Brazil who can't access institutional research infrastructure
• Any medical professional with access to an LLM who wants to generate evidence-based hypotheses

Important disclaimers

• Not for clinical use. hypokrates generates hypotheses, not diagnoses.
• PRR is not absolute risk. Disproportionality measures detect reporting patterns, not causation.
• FAERS is voluntary reporting. Underreporting is systematic. Absence of signal does not mean absence of risk.
• Cross-country comparison requires caution. Different reporting cultures, populations, and healthcare systems.
• Every output includes explicit limitations and confidence levels.

Status

Alpha (v0.7.0) — 1349 tests, mypy strict, ruff clean. Under active development.

License

AGPL-3.0-only — Public data, public code, public benefit.

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"First, do no harm." — Hippocratic Oath

"First, make the data accessible." — hypokrates