Blood-brain barrier penetration from structure

Classify whether a compound crosses the blood-brain barrier, scored by AUROC on TDC’s scaffold split of 1,975 drugs.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# Blood-brain barrier penetration from structure
	2	+
	3	+The blood-brain barrier keeps most foreign molecules out of the brain. Drugs for the central nervous system have to cross it; drugs for everything else usually should not. This benchmark asks which way a compound goes, from its structure.
	4	+
	5	+## Task
	6	+
	7	+Given a drug as a SMILES string, predict the binary blood-brain barrier penetration label: $1$ if the compound penetrates, $0$ if it does not. Submissions are scored by AUROC on TDC’s held-out test set, so higher is better.
	8	+
	9	+## Background
	10	+
	11	+Martins et al. (2012) built this set for a Bayesian model of blood-brain barrier penetration, and it later travelled into MoleculeNet as "BBBP"; TDC uses 1,975 of its compounds. The task matters in both directions — getting a neurological drug in, and keeping a peripheral drug out of the brain to avoid central side effects — which is why it appears in nearly every ADMET model comparison. It is also the most contested endpoint in this group, with 26 submissions.
	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 `bbb_martins` folder holds 1,624 `train_val` and 406 `test` rows (2,030 rows, 1,975 distinct SMILES, matching TDC’s count of drugs); 76.4% of labels are positive.
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 406 of 2,030 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 $0.924 \pm 0.003$ (Kläser, Banaszewski et al., 2024).
	34	+- Next: CFA, $0.920 \pm 0.006$ (submitted by Nan Jiang), then MapLight, $0.916 \pm 0.001$ (Notwell and Wood, 2023).
	35	+- Twenty-six entries are listed, the most of any endpoint in the group, and the top ten are inside $0.02$ AUROC of each other — so progress here is now measured in thousandths.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean AUROC on TDC’s fixed test set that beats MiniMol (AUROC 0.924), 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	+ 0.004 AUROC, so a difference smaller than the reported standard deviation is not a result.
	45	+
	46	+## References
	47	+
	48	+1. Martins, I. F. et al., "A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling", Journal of Chemical Information and Modeling 52 (2012), 1686–1697. [doi:10.1021/ci300124c](https://doi.org/10.1021/ci300124c)
	49	+2. Wu, Z. et al., "MoleculeNet: a benchmark for molecular machine learning", Chemical Science 9 (2018), 513–530. [doi:10.1039/C7SC02664A](https://doi.org/10.1039/C7SC02664A)
	50	+3. 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	+4. 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)
	52	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, BBB_Martins](https://tdcommons.ai/benchmark/admet_group/07bbb/), 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.