Publish CardioSafe v1.0 + v1.1 paper-snapshot weights and L1000 encoder

README.md

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+---
+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}
+}
+```

l1000/l1000_encoder.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f63d810ada84f696372bebbee92d8fb9e1b1da77a2171ca88d658ab2810bda1b
+size 10214775

l1000/l1000_per_gene_pearson.json

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