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CYP3A4 inhibition from structure

Classify whether a compound inhibits the CYP3A4 enzyme, scored by AUPRC over 12,328 compounds.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# CYP3A4 inhibition from structure
	2	+
	3	+CYP3A4 metabolises a large share of marketed drugs, so a compound that blocks it can change how every other drug a patient takes is cleared. This benchmark asks whether a compound is an inhibitor, from its structure.
	4	+
	5	+## Task
	6	+
	7	+Given a compound as a SMILES string, predict the binary CYP3A4 inhibition label. Submissions are scored by the area under the precision-recall curve (AUPRC), which TDC prefers to AUROC when positives are the minority class; higher is better.
	8	+
	9	+## Background
	10	+
	11	+Veith et al. (2009) screened tens of thousands of compounds against five cytochrome P450 isozymes in a quantitative high-throughput assay; TDC turns the CYP3A4 arm into a 12,328-compound classification benchmark. Inhibition of this enzyme is the usual mechanism behind drug-drug interactions, which is why it is tested early. With over twelve thousand labelled compounds from a single assay, it is one of the few ADMET endpoints where the data, not the label noise, is the limiting factor.
	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 `cyp3a4_veith` folder holds 9,861 `train_val` and 2,467 `test` rows (12,328 distinct SMILES, matching TDC’s page); 41.5% of labels are positive, and 1,082 of the 2,467 test compounds are.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 2,467 of 12,328 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 AUPRC, the area under the precision-recall curve, computed by TDC’s `Evaluator(name='pr-auc')`; higher is better. TDC uses it in place of AUROC where the positive class is the minority, because it is the more demanding summary there. Submissions pass continuous scores.
	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 CC BY 4.0.
	30	+
	31	+## What is known
	32	+
	33	+- Best on the leaderboard: MapLight + GNN, AUPRC $0.916 \pm 0.000$, fingerprints and descriptors with graph isomorphism network features (Notwell and Wood, 2023).
	34	+- Next: ContextPred, $0.904 \pm 0.002$, and AttrMasking, $0.902 \pm 0.002$, both pretrained graph networks (Hu et al., 2020).
	35	+- Nineteen entries are listed, from AUPRC $0.916$ down to $0.654$. The spread is wider than on the small endpoints in this group, so differences here are easier to believe.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUPRC on TDC’s fixed test set that beats MapLight + GNN (AUPRC 0.916), 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	+ 0.012 AUPRC, so a difference smaller than the reported standard deviation is not a result.
	45	+
	46	+## References
	47	+
	48	+1. Veith, H. et al., "Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries", Nature Biotechnology 27 (2009), 1050–1055. [doi:10.1038/nbt.1581](https://doi.org/10.1038/nbt.1581)
	49	+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)
	50	+3. Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V. and Leskovec, J., "Strategies for Pre-training Graph Neural Networks", ICLR (2020), arXiv:1905.12265. [arxiv.org/abs/1905.12265](https://arxiv.org/abs/1905.12265)
	51	+4. Huang, K. et al., "Artificial intelligence foundation for therapeutic science", Nature Chemical Biology 18 (2022), 1033–1036. [doi:10.1038/s41589-022-01131-2](https://doi.org/10.1038/s41589-022-01131-2)
	52	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, CYP3A4_Veith](https://tdcommons.ai/benchmark/admet_group/12cyp3a4i/), 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.