CrossAbSense — antibody developability oracles (v0.9)
Property-specific neural oracles that predict five biophysical developability assays for therapeutic IgGs from paired VH/VL sequences, combining frozen protein-language-model encoders (ESM-Cambrian, ProtT5) with configurable attention decoders.
Code: https://github.com/SimonCrouzet/CrossAbSense Dataset: GDPa1 (242 IgGs, Ginkgo Bioworks)
Each property folder (<PROPERTY>_<config-checksum>/) contains:
final.ckpt (model trained on all data — used by predict.py), fold0-4.ckpt
(5-fold CV checkpoints), config.yaml, and property.txt.
Performance (5-fold cluster-stratified CV, Spearman ρ)
| Property | This release (v0.9) | Paper (Table 1) |
|---|---|---|
| HIC (hydrophobicity) | 0.685 | 0.644 |
| Titer (expression) | 0.425 | 0.428 |
| PR_CHO (polyreactivity) | 0.461 | 0.475 |
| AC-SINS (self-association) | 0.420 | 0.475 |
| Tm2 (thermostability) | 0.442 | 0.387 |
⚠️ Important caveat (v0.9)
These weights were trained from the published configs but in an environment without
BioPhi (OASis humanness) and ScaLoP available. Those two antibody-feature sources were
substituted with sentinel values during training, so the feature inputs differ slightly
from the paper runs. This mainly affects AC-SINS (~0.05 below paper); the other four
properties match or exceed Table 1. A future v1.0 will retrain the feature-using
properties with BioPhi/ScaLoP restored. Pin revision="v0.9" if you need exactly these weights.
Usage
pip install huggingface_hub
python scripts/download_models.py --revision v0.9 # final.ckpt only (add --folds for CV)
python src/predict.py --input inputs/my_seqs.csv --model models/HIC_3595cc57 --output preds.csv
By default only final.ckpt (+ small metadata) is downloaded; the 5 CV fold
checkpoints are fetched only when you ask for them (--folds, or predict.py --use-cv/--fold).
Or let predict.py fetch on demand:
python src/predict.py --input inputs/my_seqs.csv --model HIC_3595cc57 --from-hf --output preds.csv
License
Apache-2.0, matching the CrossAbSense repository.