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Drug-induced liver injury from structure

Classify whether a drug can cause liver injury, scored by AUROC on an FDA-derived set of 475 drugs.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# Drug-induced liver injury from structure
	2	+
	3	+Liver injury has been the single most common reason for withdrawing an approved drug from the market, and it is notoriously hard to see coming. This benchmark asks whether a drug can cause it, from structure alone, on a small set curated from FDA records.
	4	+
	5	+## Task
	6	+
	7	+Given a drug as a SMILES string, predict whether it can cause liver injury ($1$) 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 aggregated from the US FDA’s National Center for Toxicological Research and used by Xu et al. (2015) for a deep-learning model of drug-induced liver injury; TDC includes its 475 drugs, the smallest benchmark in the ADMET group. Liver toxicity is often idiosyncratic — dependent on dose, metabolism and the patient rather than on structure alone — so high scores here deserve scepticism, and the endpoint is as much a test of honest evaluation as of chemistry.
	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 `dili` folder holds 379 `train_val` and 96 `test` rows (475 distinct SMILES, matching TDC’s page, with no duplicates); 49.7% of labels are positive, and 50 of the 96 test drugs are.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 96 of 475 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: MiniMol, AUROC $0.956 \pm 0.006$ (Kläser, Banaszewski et al., 2024).
	34	+- Next: ZairaChem, $0.925 \pm 0.005$ (Turon et al., 2023), then AttrMasking, $0.919 \pm 0.008$ (Hu et al., 2020).
	35	+- Twenty entries are listed, from AUROC $0.956$ to $0.700$. The top score stands out from the pack, but on 96 test drugs one AUROC point is worth about half a compound, so the ranking below first place is close to arbitrary.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUROC on TDC’s fixed test set that beats MiniMol (AUROC 0.956), 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	+ 0.031 AUROC, on a test set of 96 drugs, so a difference smaller than the reported standard deviation is not a result.
	45	+- Per-seed scores, and ideally a check on drugs withdrawn for hepatotoxicity that are not in the dataset, since 96 test compounds cannot settle this endpoint on their own.
	46	+
	47	+## References
	48	+
	49	+1. Xu, Y. et al., "Deep Learning for Drug-Induced Liver Injury", Journal of Chemical Information and Modeling 55 (2015), 2085–2093. [doi:10.1021/acs.jcim.5b00238](https://doi.org/10.1021/acs.jcim.5b00238)
	50	+2. Kläser, K., Banaszewski, B. et al., "MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning", arXiv:2404.14986 (2024). [arxiv.org/abs/2404.14986](https://arxiv.org/abs/2404.14986)
	51	+3. Turon, G. et al., "First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa", Nature Communications 14 (2023), 5736. [doi:10.1038/s41467-023-41512-2](https://doi.org/10.1038/s41467-023-41512-2)
	52	+4. 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)
	53	+
	54	+## Source
	55	+
	56	+Imported from [TDC ADMET benchmark group, DILI](https://tdcommons.ai/benchmark/admet_group/22dili/), 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.