Update README.md

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-4.0
+language:
+- en
+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
+
+- `AptaBench_dataset.csv`: main aptamer–ligand interaction table.
+- `stratified.json`: stratified in-distribution 5-fold split.
+- `disjoint_molecule.json`: molecule-disjoint 5-fold split.
+- `disjoint_aptamer.json`: aptamer-disjoint 5-fold split.
+
+## 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`.