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Human intestinal absorption from structure

Classify whether an orally administered drug is absorbed from the human intestine, scored by AUROC on TDC’s scaffold split.

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README.md

Human intestinal absorption from structure

A pill only works if the gut lets the drug through. This benchmark asks, from the structure alone, whether a compound is absorbed from the human gastrointestinal tract into the bloodstream.

Task

Given a drug as a SMILES string, predict the binary human intestinal absorption (HIA) label: 11 for absorbed, 00 for not. Submissions are scored by the area under the receiver operating characteristic curve (AUROC), which is the probability that a randomly chosen positive compound is ranked above a randomly chosen negative one, so higher is better.

Background

Hou et al. (2007) compiled the HIA set as part of a series of ADME evaluation studies, labelling compounds by whether oral absorption exceeded a threshold. TDC includes its 578 drugs in the ADMET group, where it is one of the smallest and most skewed classification tasks: about 87% of the compounds are absorbed. It is useful less as a hard prediction problem than as a check that a model does not fall over on a small, imbalanced set.

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 hia_hou folder holds 461 train_val and 117 test rows (578 drugs, matching TDC’s page); 86.5% of all labels are positive, and 90 of the 117 test compounds are.

The split is a scaffold split: TDC holds out a fixed 20% test set (here 117 of 578 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 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.

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 "Not Specified. CC BY 4.0".

What is known

  • Best on the leaderboard: MiniMol, AUROC 0.993±0.0050.993 \pm 0.005, a 10M-parameter pretrained molecular featuriser with a small head per task (Kläser, Banaszewski et al., 2024).
  • Next: DeepMol (AutoML), 0.990±0.0020.990 \pm 0.002, then MapLight + GNN, 0.989±0.0010.989 \pm 0.001.
  • Twenty entries are listed, and the task is close to saturated: the top thirteen all sit above 0.960.96, within a few hundredths of each other, on a test set of 117 compounds with 27 negatives. Differences at the top are about the size of one or two test compounds changing side.

What would count as progress

  • A mean AUROC on TDC’s fixed test set that beats MiniMol (AUROC 0.993), 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 0.003 AUROC, so a difference smaller than the reported standard deviation is not a result.
  • Because the task is near its ceiling, a submission is more convincing if it also reports precision and recall on the 27 test negatives, where the remaining errors are.

References

  1. Hou, T. et al., "ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification", Journal of Chemical Information and Modeling 47 (2007), 208–218. doi:10.1021/ci600343x
  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
  3. 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

Source

Imported from TDC ADMET benchmark group, HIA_Hou, 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.