Lipophilicity from structure

Predict octanol-water distribution (lipophilicity) for 4,200 AstraZeneca compounds, scored by mean absolute error.

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State the problem, from Therapeutics Data Commons ADMET benchmark group

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	1	+# Lipophilicity from structure
	2	+
	3	+Lipophilicity — how a compound divides itself between oil and water — sets much of the rest of a drug’s behaviour: how it dissolves, how fast it is metabolised, where it ends up. This benchmark asks for the measured value from the structure.
	4	+
	5	+## Task
	6	+
	7	+Given a compound as a SMILES string, predict its lipophilicity, reported by TDC as a log-ratio (the labels run from $-1.5$ to $4.5$). Submissions are scored by mean absolute error on the held-out test set, so lower is better.
	8	+
	9	+## Background
	10	+
	11	+The dataset comes from AstraZeneca’s 2016 release of in vitro DMPK and physicochemical measurements on publicly disclosed compounds, and reached wide use through MoleculeNet; TDC includes 4,200 of those compounds. It is measured on one assay in one laboratory, which makes it unusually consistent for a public ADMET set — the residual error is closer to real model error than to disagreement between data sources.
	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 `lipophilicity_astrazeneca` folder holds 3,360 `train_val` and 840 `test` rows (4,200 distinct SMILES, matching TDC’s page).
	17	+
	18	+The split is a scaffold split: TDC holds out a fixed 20% test set (here 840 of 4,200 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 "Not Specified. CC BY 4.0".
	30	+
	31	+## What is known
	32	+
	33	+- Best on the leaderboard: MiniMol, MAE $0.456 \pm 0.008$ (Kläser, Banaszewski et al., 2024).
	34	+- Next: Chemprop-RDKit, $0.467 \pm 0.006$, then plain Chemprop, $0.470 \pm 0.009$ (Yang et al., 2019).
	35	+- Twenty-one entries are listed, from MAE $0.456$ to $0.743$. The leading four sit within $0.025$ of each other, and graph neural networks hold all of them.
	36	+
	37	+## What would count as progress
	38	+
	39	+- A mean MAE on TDC’s fixed test set that beats MiniMol (MAE 0.456), 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	+ 0.011 log units, so a difference smaller than the reported standard deviation is not a result.
	45	+
	46	+## References
	47	+
	48	+1. AstraZeneca, "Experimental in vitro DMPK and physicochemical data on a set of publicly disclosed compounds" (2016), deposited in ChEMBL as AZ_dataset. [doi:10.6019/CHEMBL3301361](https://doi.org/10.6019/CHEMBL3301361)
	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. 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	+
	53	+## Source
	54	+
	55	+Imported from [TDC ADMET benchmark group, Lipophilicity_AstraZeneca](https://tdcommons.ai/benchmark/admet_group/05lipo/), 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.