CYP3A4 inhibition from structure

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.

Task

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.

Background

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.

Data and evaluation

TDC ships the group as one archive (admet_group, Harvard Dataverse file 4426004), loaded with 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.

The split is a scaffold split: TDC holds out a fixed 20% test set (here 2,467 of 12,328 rows in the group’s files), and the remaining train_val part is split again per seed by group.get_train_valid_split(benchmark=name, split_type='default', seed=seed) into 87.5% training and 12.5% validation. The leaderboard asks for five independent runs with seeds 1, 2, 3, 4 and 5; group.evaluate_many then reports the mean and the standard deviation of the per-seed test scores, rounded to three decimals. The test set never changes, so only the training split moves with the seed.

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.

Official evaluation code: tdc/benchmark_group/base_group.py in the TDC repository (MIT licence), whose evaluate and evaluate_many methods are what the leaderboard numbers come from. The dataset page gives the licence as CC BY 4.0.

What is known

Best on the leaderboard: MapLight + GNN, AUPRC $0.916 \pm 0.000$ , fingerprints and descriptors with graph isomorphism network features (Notwell and Wood, 2023).
Next: ContextPred, $0.904 \pm 0.002$ , and AttrMasking, $0.902 \pm 0.002$ , both pretrained graph networks (Hu et al., 2020).
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.

What would count as progress

A mean AUPRC on TDC’s fixed test set that beats MapLight + GNN (AUPRC 0.916), averaged over the official seeds 1-5 and produced by group.evaluate_many, with the five per-seed numbers reported as well.
The training and inference code, the environment and the fitted model, so that the five runs can be repeated end to end from the train_val part alone.
Evidence that the gain is larger than run-to-run noise: the gap between the top two entries here is 0.012 AUPRC, so a difference smaller than the reported standard deviation is not a result.

References

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
Notwell, J. H. and Wood, M. W., "ADMET property prediction through combinations of molecular fingerprints", arXiv:2310.00174 (2023). arxiv.org/abs/2310.00174
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
Huang, K. et al., "Artificial intelligence foundation for therapeutic science", Nature Chemical Biology 18 (2022), 1033–1036. doi:10.1038/s41589-022-01131-2

Source

Imported from TDC ADMET benchmark group, CYP3A4_Veith, 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.