structflo-ner 0.1.1

Drug discovery NER wrapper around LangExtract — zero-config entity extraction for chemistry and biology.

structflo.ner

Drug discovery NER powered by LangExtract.

Extract compounds, targets, bioactivity data, diseases, and more from scientific text — zero configuration required.

Install

pip install structflo-ner
# or with uv
uv add structflo-ner

# optional pandas support
pip install "structflo-ner[dataframe]"

Quick start

from structflo.ner import NERExtractor

extractor = NERExtractor(api_key="YOUR_GEMINI_KEY")
result = extractor.extract(
    "Gefitinib (ZD1839) is a first-generation EGFR inhibitor with IC50 = 0.033 µM approved for NSCLC."
)

print(result.compounds)      # [ChemicalEntity(text='Gefitinib', ...)]
print(result.targets)        # [TargetEntity(text='EGFR', ...)]
print(result.bioactivities)  # [BioactivityEntity(text='IC50 = 0.033 µM', ...)]
print(result.diseases)       # [DiseaseEntity(text='NSCLC', ...)]

df = result.to_dataframe()   # flat pandas DataFrame

Local models via Ollama

Run extraction entirely on your own hardware — no API key needed:

extractor = NERExtractor(
    model_id="gemma3:27b",
    model_url="http://localhost:11434",
)
result = extractor.extract("Sorafenib inhibits VEGFR-2 and RAF kinases.")

Any model served by Ollama works (gemma, llama, mistral, qwen, deepseek, etc.).

Built-in profiles

Profile	Entity classes
FULL (default)	compounds, targets, diseases, bioactivities, assays, mechanisms
CHEMISTRY	compound names, SMILES, CAS numbers, molecular formulas
BIOLOGY	targets, gene names, protein names
BIOACTIVITY	bioactivity measurements, assays
DISEASE	diseases and clinical indications

from structflo.ner import NERExtractor, CHEMISTRY

extractor = NERExtractor(api_key="YOUR_GEMINI_KEY")
result = extractor.extract(text, profile=CHEMISTRY)

Profiles can be merged:

from structflo.ner import CHEMISTRY, BIOLOGY

combined = CHEMISTRY.merge(BIOLOGY)
result = extractor.extract(text, profile=combined)

Custom profiles

from structflo.ner import NERExtractor, EntityProfile

my_profile = EntityProfile(
    name="kinase_inhibitors",
    entity_classes=["compound_name", "smiles", "target", "bioactivity"],
    prompt="Extract kinase inhibitor names, SMILES, targets, and potency values.",
    examples=my_examples,
)
result = extractor.extract(text, profile=my_profile)

Visualization

Render results as color-coded, interactive HTML directly in Jupyter notebooks:

# Auto-renders when result is the last expression in a cell
result

# Or call explicitly
result.display()

Each entity type gets a distinct color with a superscript label. Hover any highlight to see its type and attributes. Click legend items to toggle categories on or off.

To get the raw HTML string (useful outside Jupyter):

from structflo.ner import render_html

html_str = render_html(result)

Working with results

result.all_entities()   # flat list of every entity
result.to_dict()        # plain dictionary
result.to_dataframe()   # pandas DataFrame (requires structflo-ner[dataframe])

Notebooks

See the notebooks/ directory for worked examples:

• 01_quickstart.ipynb — end-to-end extraction with cloud and local models