Ames mutagenicity from structure

Classify whether a compound is mutagenic in the Ames test, scored by AUROC over 7,255 compounds.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# Ames mutagenicity from structure
	2	+
	3	+The Ames test exposes bacteria to a compound and looks for mutations; a positive result usually ends a drug programme. This benchmark asks for the outcome of that assay from the structure alone, over the largest public collection of Ames results.
	4	+
	5	+## Task
	6	+
	7	+Given a compound as a SMILES string, predict whether it is mutagenic ($1$) or not ($0$) in the Ames assay. Submissions are scored by AUROC, so higher is better.
	8	+
	9	+## Background
	10	+
	11	+The Ames bacterial reverse mutation assay is the standard first screen for genetic toxicity, required by regulators before a compound reaches people. Xu et al. (2012) aggregated results from four published collections into the set TDC uses, 7,255 compounds, which is large and almost balanced (54.6% mutagenic). Because mutagenicity is often driven by specific reactive substructures, this endpoint is a good test of whether a model has learned chemistry rather than a global average.
	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 `ames` folder holds 5,821 `train_val` and 1,457 `test` rows (7,278 rows, 7,255 distinct SMILES, matching TDC’s page); 870 of the 1,457 test compounds are mutagenic.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 1,457 of 7,278 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: ZairaChem, AUROC $0.871 \pm 0.002$, an automated QSAR pipeline (Turon et al., 2023).
	34	+- Next: ADMETrix, $0.870 \pm 0.006$, then MapLight + GNN, $0.869 \pm 0.002$ — the top three are separated by $0.002$ AUROC.
	35	+- Twenty entries are listed, from AUROC $0.871$ to $0.716$. The leaderboard has been flat near $0.87$ across quite different methods, which suggests the remaining error is in the data as much as in the models.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUROC on TDC’s fixed test set that beats ZairaChem (AUROC 0.871), 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	+ 0.001 AUROC, so a difference smaller than the reported standard deviation is not a result.
	45	+- A check that the gain holds on compounds whose scaffolds are absent from the training split, since structural alerts transfer easily and can flatter a model.
	46	+
	47	+## References
	48	+
	49	+1. Xu, C. et al., "In silico Prediction of Chemical Ames Mutagenicity", Journal of Chemical Information and Modeling 52 (2012), 2840–2847. [doi:10.1021/ci300400a](https://doi.org/10.1021/ci300400a)
	50	+2. 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)
	51	+3. 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)
	52	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, AMES](https://tdcommons.ai/benchmark/admet_group/21ames/), 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.