---
license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - chemistry
  - drug-discovery
  - cardiotoxicity
  - hERG
  - Nav1.5
  - Cav1.2
  - IKs
  - ion-channels
  - QSAR
  - ChEMBL
  - cipa
  - benchmark
pretty_name: CardioSafe ion-channel benchmark
size_categories:
  - 100K<n<1M
configs:
  - config_name: tan70_v1_0
    data_files:
      - split: train
        path: tan70_v1_0/train.parquet
      - split: validation
        path: tan70_v1_0/validation.parquet
      - split: test
        path: tan70_v1_0/test.parquet
  - config_name: tan60_v1_0
    data_files:
      - split: train
        path: tan60_v1_0/train.parquet
      - split: validation
        path: tan60_v1_0/validation.parquet
      - split: test
        path: tan60_v1_0/test.parquet
  - config_name: tan70_v1_1
    data_files:
      - split: train
        path: tan70_v1_1/train.parquet
      - split: validation
        path: tan70_v1_1/validation.parquet
      - split: test
        path: tan70_v1_1/test.parquet
  - config_name: tan60_v1_1
    data_files:
      - split: train
        path: tan60_v1_1/train.parquet
      - split: validation
        path: tan60_v1_1/validation.parquet
      - split: test
        path: tan60_v1_1/test.parquet
---

# CardioSafe ion-channel benchmark

Curated multi-task ion-channel labels + Tanimoto-controlled splits +
supplementary materials 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)).

The canonical source is the
[CardioSafe-benchmark GitHub repository](https://github.com/AppliedScientific/CardioSafe-benchmark).
This HF Datasets repo mirrors `data/` from that deposit and adds
pre-joined per-fold Parquet shards so you can write:

```python
from datasets import load_dataset

# v1.1 (audit-clean) test fold on the tan70 Tanimoto cutoff:
ds = load_dataset("appliedscientific/cardiosafe-benchmark",
                  "tan70_v1_1", split="test")
print(ds.column_names)
# ['row_idx', 'smiles', 'inchikey', 'herg_pchembl', 'herg_blocker_10um',
#  'herg_blocker_1um', 'nav15_pchembl', 'nav15_blocker', 'cav12_pchembl',
#  'cav12_blocker', 'iks_blocker']
```

Each row is one curated compound. Label columns are NaN-sparse — only
those compounds with primary-screen evidence for the relevant channel
have non-null values.

## Configs (pre-joined splits)

| Config | Split strategy | Version | Compounds (train / val / test) |
| --- | --- | --- | --- |
| `tan70_v1_0` | Tanimoto ≥ 0.70 cutoff | v1.0 preprint | 241,792 / 46,326 / 46,326 |
| `tan60_v1_0` | Tanimoto ≥ 0.60 cutoff | v1.0 preprint | 306,665 / 13,889 / 13,889 |
| `tan70_v1_1` | Tanimoto ≥ 0.70 cutoff | v1.1 retrain | 241,790 / 46,328 / 46,326 |
| `tan60_v1_1` | Tanimoto ≥ 0.60 cutoff | v1.1 retrain | 306,662 / 13,892 / 13,889 |

**v1.1 differs from v1.0 by 2 force-routed analogs in the cardiac-cliff
cluster** (terfenadine/fexofenadine/HMT). The test fold is identical
across v1.0 and v1.1 — paper Table 2/3 metrics are unchanged. See
[`supplementary/note_s3_v1_1_audit_correction.md`](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/data/supplementary/note_s3_v1_1_audit_correction.md).

## Label schema

| Column | Head | Type |
| --- | --- | --- |
| `herg_pchembl` | regression — hERG pIC50 | float |
| `herg_blocker_10um` | hERG blocker @ 10 µM | binary {0, 1} |
| `herg_blocker_1um` | hERG blocker @ 1 µM | binary {0, 1} |
| `nav15_pchembl` | regression — Nav1.5 pIC50 | float |
| `nav15_blocker` | Nav1.5 blocker @ 10 µM | binary {0, 1} |
| `cav12_pchembl` | regression — Cav1.2 pIC50 | float |
| `cav12_blocker` | Cav1.2 blocker @ 10 µM | binary {0, 1} |
| `iks_blocker` | IKs blocker @ 10 µM (exploratory) | binary {0, 1} |

IKs has no regression head (n = 115 labelled compounds; treated as
exploratory).

Per-channel label counts (primary binary head, all folds combined):

| Channel | n labelled | n blockers |
| --- | --- | --- |
| hERG (10 µM) | 331,127 | 11,881 |
| Nav1.5 (10 µM) | 3,160 | 1,240 |
| Cav1.2 (10 µM) | 1,138 | 548 |
| IKs (10 µM) | 115 | 30 |

## Raw canonical files

`raw/` contains the source-of-truth files exactly as published in the
GitHub repo, for power users doing custom merges:

```
raw/
├── compounds.parquet              # 334,444 × (row_idx, smiles, inchikey)
├── labels.parquet                 # 334,444 × 8 sparse labels
├── splits/
│   ├── tan70.parquet              # v1.0 — paper preprint splits
│   ├── tan60.parquet
│   ├── tan70_v1_1.parquet         # v1.1 — audit-clean retrain splits
│   └── tan60_v1_1.parquet
├── labels_MANIFEST.json           # curation provenance (sources, voting policy)
├── splits_CHANGELOG.md            # v1.0 → v1.1 diff
└── README.md                      # full data-deposit notes from GitHub
```

All splits share the same `row_idx` keying — join on it for arbitrary
slicing.

## Comparators

`comparators/` ships the CToxPred2 and CardioGenAI predictions on the
v1.0 `tan70` test fold, the inputs to the reverse-leak audit and the
head-to-head comparison in paper Tables 3 / 3b / S2 / S3 / Figure 4.

## Supplementary materials

`supplementary/` ships verbatim:
- **Notes S1, S2, S3** — Y-randomization mechanism; activity-cliff
  curation provenance + bibliography + per-pair composition + filtered
  cliff manifests; audit-driven v1.1 correction + retrain metrics + the
  cardiac-cliff case study
- **Tables S0, S1, S2, S3, S5, S6, S7, S8, S9** — descriptor spec,
  per-head confusion matrices, comparator panels pre/post de-leak, tan60
  drug panel, failure-mode SMILES, AD per-bin metrics, L1000 threshold
  sweep, curation sensitivity (no S4 — paper supplementary numbering
  jumps S3 → S5)
- **Figure S1** — hERG reliability curves across applicability-domain
  bins (PDF + PNG + JSON of the underlying decile data)

## Source data

- **Labels** are derived from ChEMBL 36 (source dump SHA-256
  `b25820eef0f0481ad7712bdf4bac3b45f354e3cbacb76be1fdbf4205d6b48fb9`,
  available from <https://www.ebi.ac.uk/chembl/>) and the **hERG Central
  primary screen**, under a pharmacology-aware curation policy that
  retains censored values and inhibition-percentage votes. Full
  provenance lives in `raw/labels_MANIFEST.json`.
- **Splits** are Tanimoto-controlled across train / val / test on
  Morgan-r2-2048 fingerprints, with terfenadine and fexofenadine
  force-routed to `val` so the canonical cardiac activity cliff is
  available as a held-out case study. v1.1 additionally force-routes the
  hydroxymethyl-terfenadine analogs flagged by an exhaustive
  O(n_train × n_other) Tanimoto leakage audit
  (`scripts/audit_tanimoto_leak.py` in the GitHub repo).

## What is **not** here

- **L1000 raw signatures** used to train the expression encoder —
  available from GEO under GSE92742 (Phase I) and GSE70138 (Phase II).
- **Continually-updated production ensemble** — served at
  [platform.appliedscientific.ai/cardiosafe](https://platform.appliedscientific.ai/cardiosafe).
  This deposit is the frozen paper snapshot.
- **Model weights** — at
  [`appliedscientific/cardiosafe`](https://huggingface.co/appliedscientific/cardiosafe)
  (5-seed v1.0 + v1.1 ensembles + L1000 encoder, CC-BY-NC-4.0).
- **Interactive demo** — at
  [`appliedscientific/cardiosafe` (Space)](https://huggingface.co/spaces/appliedscientific/cardiosafe).

## License

CC-BY-4.0. Use with attribution; commercial use allowed under the
license terms. See the
[full LICENSE-DATA](https://github.com/AppliedScientific/CardioSafe-benchmark/blob/main/LICENSE-DATA)
in the GitHub repo for the exact text.

Note: the *model weights* are CC-BY-NC-4.0 (non-commercial), not the
data. They live at a separate HF repo —
[`appliedscientific/cardiosafe`](https://huggingface.co/appliedscientific/cardiosafe).

## 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}
}
```