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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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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.