| --- |
| license: cc-by-4.0 |
| language: |
| - en |
|
|
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: dataset/AptaBench_dataset.csv |
|
|
| pretty_name: AptaBench |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| tags: |
| - aptamers |
| - small-molecules |
| - dna |
| - rna |
| - molecular-machine-learning |
| - binding-prediction |
| - affinity-prediction |
| - benchmark |
| - leakage-aware-evaluation |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| <p align="center"> |
| <img src="logo.png" width="500"> |
| </p> |
|
|
|
|
| # AptaBench |
|
|
| AptaBench is a benchmark for aptamer–small-molecule interaction prediction. It contains curated DNA/RNA aptamer–ligand pairs with standardized sequences, canonical SMILES, experimentally grounded active/inactive labels, quantitative affinity values where available, and fixed leakage-aware evaluation splits. |
|
|
| This repository is provided for anonymous peer review. Author identities, affiliations, acknowledgements, citation information, and non-anonymous project links will be added after the review process. |
|
|
| ## Dataset summary |
|
|
| The current release contains 6,289 aptamer–ligand records covering 1,610 unique aptamer sequences and 942 unique ligands from eight curated sources. |
|
|
| The benchmark supports two tasks: |
|
|
| - **Binding classification**: predict active vs inactive aptamer–ligand pairs. |
| - **Affinity regression**: predict pKd values for entries with quantitative affinity annotations. |
|
|
| Inactive labels are based on reported non-binding or low-affinity observations, not synthetic random cross-pairing. |
|
|
| ## Files |
|
|
| - `dataset/AptaBench_dataset.csv`: main aptamer–ligand interaction table in CSV format. |
| - `dataset/AptaBench_dataset.parquet`: parquet version of the dataset for efficient loading and processing. |
| - `dataset/splits/`: directory containing fixed leakage-aware evaluation splits. |
| - `stratified.json`: stratified in-distribution 5-fold split. |
| - `disjoint_molecule.json`: molecule-disjoint 5-fold split. |
| - `disjoint_aptamer.json`: aptamer-disjoint 5-fold split. |
| - `dataset/make_splits.py`: script used to generate evaluation splits. |
|
|
| ## Data fields |
|
|
| - `type`: aptamer type, DNA or RNA. |
| - `sequence`: standardized aptamer sequence. |
| - `canonical_smiles`: canonical ligand SMILES. |
| - `pKd_value`: transformed dissociation constant value, where available. |
| - `label`: binary activity label; `1` = active, `0` = inactive or low-affinity. |
| - `buffer`: reported experimental buffer or assay condition. |
| - `origin`: source publication or database record. |
| - `source`: curated source name. |
|
|
| ## Evaluation splits |
|
|
| Each split file contains five folds with: |
|
|
| - `fold`: fold identifier. |
| - `train_idx`: zero-based row indices for training. |
| - `val_idx`: zero-based held-out row indices used for validation or testing. |
|
|
| The indices refer to rows in `AptaBench_dataset.csv`. |
|
|
| The three protocols are: |
|
|
| - **Stratified**: in-distribution evaluation. |
| - **Molecule-disjoint**: held-out ligands do not appear in the corresponding training fold. |
| - **Aptamer-disjoint**: held-out aptamer sequences do not appear in the corresponding training fold. |
|
|
| Random splits are not recommended because they may overestimate generalization by allowing recurring ligands or aptamer sequences across train and held-out data. |
|
|
| ## Loading example |
|
|
| ```python |
| import json |
| import pandas as pd |
| |
| data = pd.read_csv("AptaBench_dataset.csv") |
| |
| with open("stratified.json", "r") as f: |
| splits = json.load(f) |
| |
| fold0 = splits[0] |
| train_df = data.iloc[fold0["train_idx"]] |
| test_df = data.iloc[fold0["val_idx"]] |
| ``` |
|
|
| ## Recommended metrics |
|
|
| For classification, report ROC-AUC, PR-AUC, accuracy, balanced accuracy, F1-score, precision, and recall. |
|
|
| For affinity regression, report R², RMSE, MAE, and Spearman correlation on entries with available `pKd_value`. |
