Drug half-life from structure

A drug’s half-life — how long the body takes to clear half of it — decides whether it is taken once a day or four times. It depends on metabolism, distribution and excretion together, which makes it one of the hardest ADMET properties to predict from structure.

Task

Given a drug as a SMILES string, predict its human half-life in hours. Because the property depends on factors beyond the chemical structure, TDC scores this benchmark by Spearman’s rank correlation between predicted and measured values rather than by absolute error, so higher is better and only the ordering counts.

Background

Obach, Lombardo and Waters (2008) analysed intravenous pharmacokinetic parameters for 670 drugs in humans, and TDC takes 667 of them, through the deposited ChEMBL assay 1614674, as the half-life benchmark. The labels span four orders of magnitude, from minutes to weeks (the files run from $0.065$ to $1200$ hours, median $4.2$), and human pharmacokinetics is measured in people rather than in a dish — so the irreducible noise is high and a rank metric is the honest choice.

Data and evaluation

TDC ships the group as one archive (admet_group, Harvard Dataverse file 4426004), loaded with admet_group(path='data/') from the tdc.benchmark_group module. The group’s half_life_obach folder holds 532 train_val and 135 test rows (667 rows, 665 distinct SMILES, matching TDC’s count of drugs).

The split is a scaffold split: TDC holds out a fixed 20% test set (here 135 of 667 rows in the group’s files), and the remaining train_val part is split again per seed by group.get_train_valid_split(benchmark=name, split_type='default', seed=seed) into 87.5% training and 12.5% validation. The leaderboard asks for five independent runs with seeds 1, 2, 3, 4 and 5; group.evaluate_many then reports the mean and the standard deviation of the per-seed test scores, rounded to three decimals. The test set never changes, so only the training split moves with the seed.

The metric is Spearman’s rank correlation coefficient between predicted and measured values, computed by TDC’s Evaluator(name='spearman'); higher is better. TDC uses a rank metric for endpoints that depend on factors beyond chemical structure, so a submission is judged on ordering rather than on absolute values.

Official evaluation code: tdc/benchmark_group/base_group.py in the TDC repository (MIT licence), whose evaluate and evaluate_many methods are what the leaderboard numbers come from. The dataset page gives the licence as "Not Specified. CC BY 4.0".

What is known

• Best on the leaderboard: CFA, Spearman $0.576 \pm 0.025$ (submitted by Nan Jiang).
• Next: MapLight, $0.562 \pm 0.008$, then MapLight + GNN, $0.557 \pm 0.034$ (Notwell and Wood, 2023).
• Twenty entries are listed, from Spearman $0.576$ down to $0.038$. Even the leader explains only part of the ordering, and the bottom of the board is near zero correlation: this is one of the two or three genuinely unsolved endpoints in the ADMET group.

What would count as progress

• A mean Spearman correlation on TDC’s fixed test set that beats CFA (Spearman correlation 0.576), averaged over the official seeds 1-5 and produced by group.evaluate_many, with the five per-seed numbers reported as well.
• The training and inference code, the environment and the fitted model, so that the five runs can be repeated end to end from the train_val part alone.
• Evidence that the gain is larger than run-to-run noise: the gap between the top two entries here is 0.014 Spearman, inside the leader’s own standard deviation, so a difference smaller than the reported standard deviation is not a result.
• Per-seed correlations, since on 135 test drugs a Spearman coefficient moves easily by $0.03$.
• A model that also predicts clearance and volume of distribution consistently would be more convincing than one tuned to half-life alone, because half-life is their ratio.

References

1. Obach, R. S., Lombardo, F. and Waters, N. J., "Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds", Drug Metabolism and Disposition 36 (2008), 1385–1405. doi:10.1124/dmd.108.020479
2. Notwell, J. H. and Wood, M. W., "ADMET property prediction through combinations of molecular fingerprints", arXiv:2310.00174 (2023). arxiv.org/abs/2310.00174
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

Source

Imported from TDC ADMET benchmark group, Half_Life_Obach, 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.