AptaBench: A Benchmark for Aptamer-Small Molecule Binding

Aptamers are programmable DNA and RNA receptors with broad potential in diagnostics, therapeutics, biosensing, and molecular monitoring. Although they offer chemically synthesizable alternatives to protein-based binders in several applications, computational aptamer discovery remains much less mature, especially for small-molecule targets. This gap reflects three persistent evaluation failures: fragmented and inconsistent affinity annotations, unreliable negatives derived from untested rather than experimentally validated pairs, and random splits that leak related aptamers or recurring ligand identities across train and test sets. As a result, current benchmarks make it difficult to distinguish transferable aptamer-ligand recognition from dataset-specific shortcuts. We present AptaBench, the first curated, standardized, and leakage-aware benchmark for aptamer-small-molecule binding prediction with experimentally reported active and inactive measurements and paired classification and affinity-regression tasks. AptaBench integrates eight curated sources into 6,289 interaction pairs covering 1,610 DNA/RNA aptamers and 942 ligands. We standardize sequences, resolve ligands to canonical molecular representations, harmonize duplicate and conflicting records, and convert dissociation constants to p$K_d$. More than 30% of entries contain quantitative affinity values, while inactive labels are taken only from explicitly reported non-binding or low-affinity measurements rather than synthetic cross-pairing. AptaBench provides fixed in-distribution, molecule-disjoint, and aptamer-disjoint protocols reflecting practical discovery scenarios: prediction for unseen targets and evaluation of new candidate sequences. Across descriptor-based models, pretrained sequence and molecular encoders, and sequence-ligand fusion architectures, in-distribution evaluation substantially overestimates generalization. The best classifier reaches ROC-AUC 0.95 in-distribution, but drops to 0.87 for unseen molecules and 0.86 for unseen aptamers. Affinity prediction is more sensitive, with $R^2$ decreasing from 0.65 to approximately 0.30 under disjoint protocols. These results show that reliable progress in computational aptamer discovery requires broader chemical coverage, experimentally grounded inactive labels, and standardized quantitative annotations. By releasing curated data, fixed leakage-aware splits, reproducible baselines, and preprocessing code, AptaBench is poised to expose the generalization limits of existing models, challenge future sequence-molecule recognition methods, and provide a rigorous foundation for practice-oriented aptamer evaluation.