Field	Value
Experiment	`exp09_struct_egnn_noise`
Architecture	`struct_egnn_geo`
EGNN hidden	`64`
EGNN layers	`3`
PR-AUC (calibrated)	`0.8409463785514206`
Guidance gate	`hybrid` (`gate_pass=True`)

Files

checkpoints/best.ckpt — PyTorch weights
calibrators/calibrator.pkl — isotonic calibration
train_metadata.json — experiment + struct config
metrics.json, metrics_multisigma.json, guidance_gate.json

Usage

bash

cd TFG/bbb_models && uv sync
hf download manumartinm/bbb-geo --local-dir ./bbb-geo

Structural prediction (requires coords manifest or coords_path column):

bash

uv run python scripts/geo/predict.py \
  --run-dir ./bbb-geo \
  --input candidates.parquet \
  --manifest ../dataset/data/processed/peptides_struct_manifest.parquet \
  --output scored.parquet

BoltzGen diffusion guidance:

bash

boltzgen run target.yaml \
  --protocol peptide-anything \
  --config design guidance.bbb_ckpt=/abs/path/bbb-geo/checkpoints/best.ckpt \
  --config design guidance.bbb_weight=0.3 guidance.membrane_weight=0.7

Dataset

Trained on Boltz-folded peptide structures. Companion dataset: manumartinm/bbb-peptides.

Citation

If you use this model, cite the TFG BBB structural guidance pipeline.

manumartinm/bbb-geo

BBB Geo EGNN — exp09_struct_egnn_noise

Model summary

Files

Usage

Dataset

Citation

manumartinm/
bbb-geo

BBB Geo EGNN — `exp09_struct_egnn_noise`