README.md

---
license: cc-by-nc-4.0
license_name: cc-by-nc-4.0
license_link: https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/LICENSE-WEIGHTS
library_name: pytorch
pipeline_tag: tabular-regression
tags:
  - chemistry
  - drug-discovery
  - cardiotoxicity
  - hERG
  - ion-channels
  - multi-task
  - molecules
  - QSAR
language:
  - en
---

# CardioSafe — paper-snapshot weights

Paper-snapshot weights for **CardioSafe: multi-task prediction of cardiac
ion channel activity with reverse-leak audited benchmarking** (Jovanović
et al., 2026,
[bioRxiv](https://www.biorxiv.org/content/10.64898/2026.05.06.723181v1)).

CardioSafe is a three-branch multi-task neural network that predicts
blocker status and pIC50 for the four CiPA cardiac ion channels — **hERG,
Nav1.5, Cav1.2, and (exploratory) IKs** — trained on the largest
publicly reported multi-channel cardiac ion channel dataset (ChEMBL 36 +
hERG Central, 334,444 curated compounds, 8 heads).

This HuggingFace repo is a mirror. The canonical home is
[github.com/AppliedScientific/CardioSafe-benchmark](https://github.com/AppliedScientific/CardioSafe-benchmark),
which ships the curated dataset, splits, supplementary materials, the
reverse-leak audit script, the reference model + training-step code, and
runnable inference (`inference/predict.py`). The continually-updated
deployed ensemble is served at
[platform.appliedscientific.ai/cardiosafe](https://platform.appliedscientific.ai/cardiosafe).

## Files

```
v1.0/                                     # preprint snapshot, 5-seed ensemble
  cardiosafe_v1.0_seed_{42..46}.pt        # 15 MB each
v1.1/                                     # audit-clean snapshot, 5-seed ensemble
  cardiosafe_v1.1_seed_{42..46}.pt        # 15 MB each — RECOMMENDED for new work
l1000/
  l1000_encoder.pt                        # 10 MB — shared by v1.0 + v1.1
  l1000_per_gene_pearson.json             # per-gene test-set Pearson r (diagnostic)
```

Each `.pt` contains `model_state_dict`, descriptor / L1000 / regression-head
scalers, and a clean config dict. The L1000 encoder checkpoint additionally
contains the gene co-expression `edge_index` and per-gene scaler stats.

## v1.0 vs v1.1

- **v1.0** is the exact ensemble evaluated in the bioRxiv preprint.
- **v1.1** is an audit-clean retrain: the exhaustive
  O(n_train × n_other) Tanimoto leakage audit flagged 12 train↔val edges
  in tan70 v1.0 at Morgan-r2-2048 Tanimoto ≥ 0.70, all within the
  canonical cardiac-cliff cluster (terfenadine / fexofenadine /
  hydroxymethyl-terfenadine analogs). v1.1 force-routes the 2 HMT
  analogs (rows 317153, 331406) to val so the cluster is fully
  audit-clean.
- **Test fold is identical** between v1.0 and v1.1 — headline test
  metrics (Tables 2 / 3 of the paper) are unchanged. v1.1 just gives an
  audit-clean training set for the per-seed val fold selection.
- See [Note S3](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/data/supplementary/note_s3_v1_1_audit_correction.md)
  for the full audit findings + re-evaluation of the cardiac-cliff case study.

**Use v1.1 for new work.** v1.0 is retained so the preprint numbers stay
reproducible.

## Inputs and outputs

The model expects a single flat `float32` tensor of shape `(B, 7526)`:

| dims | block | source |
| --- | --- | --- |
| 0 – 2047 | Morgan radius-2 2048-bit binary fingerprint | RDKit `GetMorganGenerator(radius=2, fpSize=2048)` |
| 2048 – 4095 | AtomPair 2048-bit binary fingerprint | RDKit `GetAtomPairGenerator(fpSize=2048)` |
| 4096 – 6143 | TopologicalTorsion 2048-bit binary fingerprint | RDKit `GetTopologicalTorsionGenerator(fpSize=2048)` |
| 6144 – 6163 | 20-descriptor block, training-fold z-scored | Spec in `data/supplementary/table_s0_descriptor_spec.*` |
| 6164 – 6547 | ChemBERTa-77M-MTR mean-pooled embedding (384) | `model/chemberta_encoder.py` |
| 6548 – 7525 | L1000 predicted expression z-scores (978) | `model/l1000_encoder.py` |

`forward(x)` returns a `dict[str, Tensor]` with 8 keys, each value a `(B,)` tensor:

| Head | Output | Channel |
| --- | --- | --- |
| `herg_pchembl` | regression — raw pIC50 | hERG |
| `herg_blocker_10um` | logit (apply sigmoid for P) | hERG |
| `herg_blocker_1um` | logit | hERG |
| `nav15_pchembl` | regression — raw pIC50 | Nav1.5 |
| `nav15_blocker` | logit | Nav1.5 |
| `cav12_pchembl` | regression — raw pIC50 | Cav1.2 |
| `cav12_blocker` | logit | Cav1.2 |
| `iks_blocker` | logit | IKs |

IKs has no regression head (n = 115 labelled compounds; treated as
exploratory). See the
[full model card](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/model/MODEL_CARD.md)
for architecture details.

## Usage

The recommended path is the runnable inference shipped in the GitHub
repo. It handles all featurization (RDKit + ChemBERTa + L1000 encoder)
and the ensemble forward pass:

```bash
git clone https://github.com/AppliedScientific/CardioSafe-benchmark
cd CardioSafe-benchmark
pip install -e .[inference]

# CSV in / CSV out — auto-downloads weights from GitHub Releases on first call
python -m inference.predict --in inference/example_smiles.csv \
                            --out predictions.csv \
                            --version v1.1
```

To download these weight files from the HuggingFace mirror instead:

```python
from huggingface_hub import snapshot_download

local = snapshot_download(repo_id="appliedscientific/cardiosafe")
# v1.0/, v1.1/, l1000/ subdirectories under `local`
```

The repo's `inference.ensemble` module loads the seed checkpoints; see
[`inference/README.md`](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/inference/README.md)
for the loader API and a Python example.

## Verified

Loading the v1.1 weights into the public
`model.cross_attn.CrossAttnIonChannelPredictor` and running the
cardiac-cliff anchors reproduces the published v1.1 case-study values to
within 0.01: terfenadine pIC50 6.258 (published 6.247), fexofenadine
pIC50 4.505 (4.512), cliff 1.754 (1.736).

## License

[CC-BY-NC-4.0](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/LICENSE-WEIGHTS).
Academic, educational, and non-profit research use is permitted with
attribution. Commercial use requires a separate license — contact the
authors (`lukas@appliedscientific.ai`, `mihailo@appliedscientific.ai`).

The code in the GitHub repository is MIT; the dataset there is CC-BY-4.0.
Only the model weights distributed here and in the GitHub Releases are
CC-BY-NC-4.0.

## Citation

```bibtex
@article{cardiosafe2026,
  title   = {CardioSafe: multi-task prediction of cardiac ion channel
             activity with reverse-leak audited benchmarking},
  author  = {Jovanović, Mihailo and Weidener, Lukas and Brkić, Marko and
             Ulgac, Emre and Meduri, Aakaash},
  year    = {2026},
  journal = {bioRxiv},
  doi     = {10.64898/2026.05.06.723181},
  url     = {https://www.biorxiv.org/content/10.64898/2026.05.06.723181v1}
}
```