Predicting protein-ligand binding affinity

Predict binding affinities for protein-ligand complexes accurately enough to be useful prospectively, and show it on benchmarks that are free of train-test leakage.

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@misc{cairn-protein-ligand-binding-affinity-prediction,
  title        = {Predicting protein-ligand binding affinity},
  author       = {{Cairn Commons contributors}},
  howpublished = {\url{https://cairn-commons.com/problems/protein-ligand-binding-affinity-prediction}},
  year         = {2026},
  note         = {Open problem on Cairn Commons, CC BY 4.0. Accessed 2026-09-28}
}

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The problem

Given a protein structure and a small molecule, predict the binding free energy (or a ranking of ligands) well enough to guide molecular design. The open question is not only accuracy on a static benchmark but whether reported accuracy survives strict separation of training and test data and prospective, blinded evaluation.

Known status. CASF-2016 (Su et al., J. Chem. Inf. Model. 2019) is the standard retrospective benchmark: the PDBbind core set of 57 clusters x 5 = 285 complexes, scored for scoring, ranking, docking and screening power. Graber et al. (Nature Machine Intelligence 2025) showed severe train-test similarity between PDBbind and CASF — nearly 600 similarities affecting 49% of CASF complexes — and that after retraining on a leakage-filtered split (CleanSplit) some published models fall towards trivial-baseline error, while a simple similarity-search baseline moves from RMSE 1.517 to 1.648. Prospective blinded evaluation is available through open challenge platforms (ASAP/Polaris/OpenADMET potency and ADMET challenges, with hundreds of participants).

What counts as progress

A model evaluated on a leakage-controlled public split (e.g. PDBbind CleanSplit) with training code, weights and split files released, improving on published baselines.
Reproducible physics-based free-energy calculations (e.g. relative binding free energies) on a public congeneric series, with inputs, force field and analysis scripts.
New or improved leakage audits: quantified similarity between a widely used training set and a benchmark, with the detection code.
Prospective submissions to an open blinded challenge, with the pre-registered method and the post-hoc scores.
Documented negative results: a reported gain that disappears under a stated de-duplication.

How it is checked. A reviewer re-runs training and evaluation on the published split and confirms the metrics (Pearson R, RMSE, ranking power), checks that no test complex or near-duplicate is in training, and for prospective work compares against the challenge organisers' scores.

Current state

The problem