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Aqueous solubility from structure

Predict aqueous solubility in log mol/L for 9,982 compounds from AqSolDB, scored by mean absolute error.

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1	# Aqueous solubility from structure
2
3	A drug that will not dissolve in water is hard to absorb and hard to dose. This benchmark asks for a compound’s aqueous solubility from its structure, over the largest curated solubility set in public use.
4
5	## Task
6
7	Given a compound as a SMILES string, predict its aqueous solubility in $\log_{10}$ mol/L. The labels in the benchmark files run from $-13.2$ to $2.14$. Submissions are scored by mean absolute error, so lower is better.
8
9	## Background
10
11	AqSolDB (Sorkun, Khetan and Er, 2019) merges nine public solubility sources into 9,982 curated entries with 2D descriptors, resolving conflicts between them; TDC uses it whole as the ADMET group’s largest regression benchmark. Solubility is the textbook target for structure-property prediction, which makes it a useful control: a model that cannot do solubility is unlikely to do the harder endpoints, and the dataset is big enough that data-hungry methods can show their advantage.
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 `solubility_aqsoldb` folder holds 7,985 `train_val` and 1,997 `test` rows (9,982 distinct SMILES, matching TDC’s page). This is the only dataset in the group whose licence TDC states outright, as CC BY 4.0.
17
18	The split is a scaffold split: TDC holds out a fixed 20% test set (here 1,997 of 9,982 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 mean absolute error, $\mathrm{MAE} = \frac{1}{n}\sum_{i=1}^{n} \|y_i - \hat{y}_i\|$, computed by TDC’s `Evaluator(name='mae')` on the test set; lower is better.
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 CC BY 4.0.
30
31	## What is known
32
33	- Best on the leaderboard: MiniMol, MAE $0.741 \pm 0.013$ (Kläser, Banaszewski et al., 2024).
34	- Next: Chemprop-RDKit, $0.761 \pm 0.025$, a message-passing network with RDKit descriptors (Yang et al., 2019), then DeepMol (AutoML), $0.775 \pm 0.006$.
35	- Eighteen entries are listed, spanning MAE $0.741$ to $1.203$. Unlike the small endpoints in this group, the ordering here is fairly stable: learned molecular representations lead, and the gaps are wider than the seed-to-seed spread.
36
37	## What would count as progress
38
39	- A mean MAE on TDC’s fixed test set that beats MiniMol (MAE 0.741), 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	0.02 log mol/L, so a difference smaller than the reported standard deviation is not a result.
45	- Solubility spans more than fifteen log units, so a submission should report error on the poorly soluble tail separately; that is the part that matters for formulation.
46
47	## References
48
49	1. Sorkun, M. C., Khetan, A. and Er, S., "AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds", Scientific Data 6 (2019), 143. [doi:10.1038/s41597-019-0151-1](https://doi.org/10.1038/s41597-019-0151-1)
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. Yang, K. et al., "Analyzing Learned Molecular Representations for Property Prediction", Journal of Chemical Information and Modeling 59 (2019), 3370–3388. [doi:10.1021/acs.jcim.9b00237](https://doi.org/10.1021/acs.jcim.9b00237)
52	4. Huang, K. et al., "Artificial intelligence foundation for therapeutic science", Nature Chemical Biology 18 (2022), 1033–1036. [doi:10.1038/s41589-022-01131-2](https://doi.org/10.1038/s41589-022-01131-2)
53
54	## Source
55
56	Imported from [TDC ADMET benchmark group, Solubility_AqSolDB](https://tdcommons.ai/benchmark/admet_group/06aqsol/), 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.