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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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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.