hERG channel blockade from structure

Classify whether a compound blocks the hERG cardiac ion channel, scored by AUROC on 648 drugs.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# hERG channel blockade from structure
	2	+
	3	+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.
	4	+
	5	+## Task
	6	+
	7	+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.
	8	+
	9	+## Background
	10	+
	11	+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 Cardiotoxicity through hERG has withdrawn marketed drugs, so the endpoint carries more consequence than most in this group.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.
	12	+
	13	+## Data and evaluation
	14	+
	15	+TDC ships the group as one archive (`admet_group`, Harvard Dataverse file 4426004), loaded with
	16	+`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.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 132 of 655 rows in
	19	+the group’s files), and the remaining `train_val` part is split again per seed by
	20	+`group.get_train_valid_split(benchmark=name, split_type='default', seed=seed)` into 87.5% training
	21	+and 12.5% validation. The leaderboard asks for five independent runs with seeds 1, 2, 3, 4 and 5;
	22	+`group.evaluate_many` then reports the mean and the standard deviation of the per-seed test scores,
	23	+rounded to three decimals. The test set never changes, so only the training split moves with the seed.
	24	+
	25	+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.
	26	+
	27	+Official evaluation code: [`tdc/benchmark_group/base_group.py`](https://github.com/mims-harvard/TDC/blob/main/tdc/benchmark_group/base_group.py)
	28	+in the TDC repository (MIT licence), whose `evaluate` and `evaluate_many` methods are what the
	29	+leaderboard numbers come from. The dataset page gives the licence as "Not Specified. CC BY 4.0".
	30	+
	31	+## What is known
	32	+
	33	+- Best on the leaderboard: MapLight + GNN, AUROC $0.880 \pm 0.002$ (Notwell and Wood, 2023).
	34	+- 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.
	35	+- 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.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUROC on TDC’s fixed test set that beats MapLight + GNN (AUROC 0.88), averaged over the official
	40	+ seeds 1-5 and produced by `group.evaluate_many`, with the five per-seed numbers reported as well.
	41	+- The training and inference code, the environment and the fitted model, so that the five runs can be
	42	+ repeated end to end from the `train_val` part alone.
	43	+- Evidence that the gain is larger than run-to-run noise: the gap between the top two entries here is
	44	+ 0.005 AUROC, less than the runners-up’ own standard deviations, so a difference smaller than the reported standard deviation is not a result.
	45	+- Per-seed scores and a confusion matrix, so a reader can tell a real gain from the noise of a 132-compound test set.
	46	+
	47	+## References
	48	+
	49	+1. 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](https://doi.org/10.1021/acs.molpharmaceut.6b00471)
	50	+2. Notwell, J. H. and Wood, M. W., "ADMET property prediction through combinations of molecular fingerprints", arXiv:2310.00174 (2023). [arxiv.org/abs/2310.00174](https://arxiv.org/abs/2310.00174)
	51	+3. 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](https://arxiv.org/abs/2102.09548)
	52	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, hERG](https://tdcommons.ai/benchmark/admet_group/20herg/), 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.