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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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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# Human intestinal absorption from structure
	2	+
	3	+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.
	4	+
	5	+## Task
	6	+
	7	+Given a drug as a SMILES string, predict the binary human intestinal absorption (HIA) label: $1$ for absorbed, $0$ 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.
	8	+
	9	+## Background
	10	+
	11	+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.
	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 `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.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 117 of 578 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: MiniMol, AUROC $0.993 \pm 0.005$, a 10M-parameter pretrained molecular featuriser with a small head per task (Kläser, Banaszewski et al., 2024).
	34	+- Next: DeepMol (AutoML), $0.990 \pm 0.002$, then MapLight + GNN, $0.989 \pm 0.001$.
	35	+- Twenty entries are listed, and the task is close to saturated: the top thirteen all sit above $0.96$, 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.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUROC on TDC’s fixed test set that beats MiniMol (AUROC 0.993), 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	+ 0.003 AUROC, so a difference smaller than the reported standard deviation is not a result.
	45	+- 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.
	46	+
	47	+## References
	48	+
	49	+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](https://doi.org/10.1021/ci600343x)
	50	+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](https://arxiv.org/abs/2404.14986)
	51	+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](https://arxiv.org/abs/2102.09548)
	52	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, HIA_Hou](https://tdcommons.ai/benchmark/admet_group/02hia/), 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.