EDKG-DL

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Deep Learning framework with causality-integrated Endocrine Disruption Knowledge Graph. Predicts endocrine-disrupting chemicals (EDCs), applicability domain (ADs), biological entities, and sensitive pathways from compound SMILES using machine learning and graph deep learning algorithmes.

Pipeline: PaDEL 2D descriptors/fingerprints → biological entity-specific qualitative/quantitative models → causality-integrated EDC classification → sensitive adverse outcome (AO) and adverse outcome pathway (AOP) inference, producing JSON / Excel reports.

Installation

Install from PyPI (Python ≥ 3.10):

pip install edkg-dl
# or
uv add edkg-dl

Optional extras: pip install "edkg-dl[api]" (HTTP service) and pip install "edkg-dl[plots]" (applicability-domain plots). See installation docs for details, including the automatic Temurin JRE download for PaDEL.

Model assets (~930 MB) are fetched automatically from Hugging Face on first use — no manual setup required.

Quick start

CLI:

edkg-dl-predict "CCO"                                   # JSON to stdout
edkg-dl-predict "CCO" -o runs/example --ad-plots        # reports + PCA plots
edkg-dl-predict batch molecules.txt -o runs/batch       # batch from a text file

Python:

from edkg_dl import Predictor

predictor = Predictor.from_assets("./models")
result = predictor.predict("CCO")

Documentation

Disclaimer

Results are for research support only and must not replace experimental or regulatory conclusions.