hERG channel blockade from structure

The hERG potassium channel helps keep the heartbeat regular, and blocking it can be fatal. Screening for hERG liability is a standard safety gate in drug discovery; this benchmark asks for the call from structure alone.

Task

Given a drug as a SMILES string, predict whether it blocks the hERG channel ( $1$ ) or not ( $0$ ). Submissions are scored by AUROC on TDC’s held-out test set, so higher is better.

Background

The set was assembled by Wang et al. (2016) for a hERG classifier built from pharmacophores and machine learning, and TDC uses its 648 drugs. Cardiotoxicity through hERG has withdrawn marketed drugs, so the endpoint carries more consequence than most in this group. TDC also hosts larger hERG datasets (hERG Central, hERG Karim), but only this one is part of the ADMET benchmark group, which is what the leaderboard scores.

Data and evaluation

TDC ships the group as one archive (admet_group, Harvard Dataverse file 4426004), loaded with admet_group(path='data/') from the tdc.benchmark_group module. The group’s herg folder holds 523 train_val and 132 test rows (655 rows, 648 distinct SMILES, matching TDC’s count of drugs); 68.9% of labels are positive.

The split is a scaffold split: TDC holds out a fixed 20% test set (here 132 of 655 rows in the group’s files), and the remaining train_val part is split again per seed by group.get_train_valid_split(benchmark=name, split_type='default', seed=seed) into 87.5% training and 12.5% validation. The leaderboard asks for five independent runs with seeds 1, 2, 3, 4 and 5; group.evaluate_many then reports the mean and the standard deviation of the per-seed test scores, rounded to three decimals. The test set never changes, so only the training split moves with the seed.

The metric is AUROC, the area under the receiver operating characteristic curve, computed by TDC’s Evaluator(name='roc-auc'); it equals the probability that a positive compound is scored above a negative one, and higher is better. Submissions pass continuous scores, not hard labels.

Official evaluation code: tdc/benchmark_group/base_group.py in the TDC repository (MIT licence), whose evaluate and evaluate_many methods are what the leaderboard numbers come from. The dataset page gives the licence as "Not Specified. CC BY 4.0".

What is known

Best on the leaderboard: MapLight + GNN, AUROC $0.880 \pm 0.002$ (Notwell and Wood, 2023).
Next: CFA, $0.875 \pm 0.014$ (submitted by Nan Jiang), then SimGCN, $0.874 \pm 0.014$ — both within their own error bars of the leader.
Twenty entries are listed, from AUROC $0.880$ to $0.715$ . With 132 test compounds, the top of this leaderboard is a statistical tie; the standard deviations of the runners-up are seven times the gap to first place.

What would count as progress

A mean AUROC on TDC’s fixed test set that beats MapLight + GNN (AUROC 0.88), averaged over the official seeds 1-5 and produced by group.evaluate_many, with the five per-seed numbers reported as well.
The training and inference code, the environment and the fitted model, so that the five runs can be repeated end to end from the train_val part alone.
Evidence that the gain is larger than run-to-run noise: the gap between the top two entries here is 0.005 AUROC, less than the runners-up’ own standard deviations, so a difference smaller than the reported standard deviation is not a result.
Per-seed scores and a confusion matrix, so a reader can tell a real gain from the noise of a 132-compound test set.

References

Wang, S. et al., "ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches", Molecular Pharmaceutics 13 (2016), 2855–2866. doi:10.1021/acs.molpharmaceut.6b00471
Notwell, J. H. and Wood, M. W., "ADMET property prediction through combinations of molecular fingerprints", arXiv:2310.00174 (2023). arxiv.org/abs/2310.00174
Huang, K. et al., "Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development", NeurIPS Datasets and Benchmarks (2021), arXiv:2102.09548. arxiv.org/abs/2102.09548

Source

Imported from TDC ADMET benchmark group, hERG, curated by Therapeutics Data Commons (Harvard). Open according to the source as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.