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Hierarchical Evaluation of Drug GEnerators tHrOugh riGorous filtration

Project description

🦔 HEDGEHOG

Hierarchical Evaluation of Drug GEnerators tHrOugh riGorous filtration

PyPI version CI License: MIT Python 3.10+

HEDGEHOG Pipeline

Comprehensive benchmark pipeline for evaluating generative models in molecular design.

Pipeline Stages:

Each stage takes the output of the previous one, progressively filtering the molecule set:

  1. Mol Prep (Datamol): salts/solvents & fragments cleanup, largest-fragment selection, metal disconnection, uncharging, tautomer canonicalization, stereochemistry removal → produces standardized “clean” molecules (molPrep folder)
  2. Molecular Descriptors: 22 physicochemical descriptors (logP, HBD/HBA, TPSA, QED, etc.) → molecules outside thresholds are removed (descriptors folder)
  3. Structural Filters: 6 criteria with ~2500 SMARTS patterns (PAINS, Glaxo, NIBR, Bredt, etc.) → flagged molecules are removed (structural filters folder)
  4. Synthesis Evaluation: SA score, SYBA score, AiZynthFinder retrosynthesis → unsynthesizable molecules are removed (synthesis folder)
  5. Molecular Docking: SMINA and/or GNINA → binding affinity scoring (docking folder)
  6. Docking Filters: post-docking pose quality filtering → poor binders are removed
  7. Final Descriptors: recalculation on the filtered set

Post-pipeline analysis: MolEval generative metrics

Setup & Run

Install from PyPI

python -m pip install hedgehog
hedgehog --help

Base install is intentionally lightweight and works on modern Python versions (including Python 3.13) without optional heavy docking extras.

Optional extras:

# Legacy PoseCheck backend for docking filters
python -m pip install 'hedgehog[docking-legacy]'

# Shepherd-Score Python dependency only (may be unavailable on some ABIs, e.g. cp313)
python -m pip install 'hedgehog[shepherd]'

Recommended Shepherd setup is an isolated worker environment:

uv run hedgehog setup shepherd-worker --yes

Install from source (recommended for development)

# Clone repository
git clone https://github.com/LigandPro/hedgehog.git
cd hedgehog


# Install AiZynthFinder (for synthesis stage)
./modules/install_aizynthfinder.sh
#
# Alternatively (recommended), install via CLI:
# uv run hedgehog setup aizynthfinder --yes

# Install package with uv
uv sync

You are ready to use 🦔 HEDGEHOG for your purpose!

Usage

# Run full pipeline on a proposed small test data from `data/test/`
uv run hedgehog run

# Alternatively, using the short-name alias:
uv run hedge run

# Run specific stage
uv run hedge run --stage descriptors

# Enable live progress bar in CLI
uv run hedge run --progress

# Get help
uv run hedge --help

Terminal UI (TUI)

For interactive configuration and pipeline management, use the TUI:

cd tui
npm install
npm run tui

See tui/README.md for details.

Unified verification pipeline

Use one command entry point for local/CI checks:

# Quick local smoke (CLI + TUI build + TUI startup/quit in PTY)
uv run python scripts/check_pipeline.py --mode quick

# CI smoke profile (same checks, no full production run)
uv run python scripts/check_pipeline.py --mode ci

# Full local verification (quick checks + full production pipeline run)
uv run python scripts/check_pipeline.py --mode full

--mode full runs uv run hedgehog run with the default production config, so it can be long-running and requires external stage dependencies (for example docking/synthesis tooling) to be installed in your local environment.

Documentation Site

cd docs && pnpm install && pnpm dev

The docs site is built with Nextra and available at http://localhost:3000.

HTML Reports

After each pipeline run, an interactive HTML report is automatically generated as report.html in the results folder. The report includes:

  • Pipeline summary and molecule retention funnel
  • Per-stage statistics and visualizations
  • Descriptor distributions
  • Filter pass/fail breakdowns
  • Synthesis scores and docking results

Configure your run Edit config for each stage in configs folder based on metrics you want to calculate.

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