Datasets:

t2ance
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DrugRNA-Data

This repository contains the RNA-small molecule interaction prediction dataset used in the paper "LLM Agents Enable RNA–Small Molecule Interaction Prediction at Performance Comparable to Human-Designed Models".

Dataset Description

The dataset is derived from the RNAInter repository and contains experimentally validated RNA-compound interactions. It includes 45,049 RNA–compound pairs spanning 3,258 unique RNAs and 345 unique compounds, with a 1:4 positive/negative ratio.

Data Splits

The dataset is organized into three evaluation scenarios:

1. In-Distribution Split (`in_distribution/`)

Purpose: Standard 80/10/10 train/validation/test split for head-to-head performance comparison
Characteristics: Complete entity overlap, testing interpolation within known molecular space
Files:
- train_text.csv (36,040 samples, 20.0% positive)
- val_text.csv (4,504 samples, 19.8% positive)
- test_text.csv (4,505 samples, 20.2% positive)
Total: 45,049 samples with 3,258 unique RNAs and 345 unique compounds

2. Full Out-of-Domain Split (`full_ood/`)

Purpose: Cold-start prediction with entirely novel entities
Characteristics: Zero overlap for both RNAs and compounds between training and test sets
Files:
- train_text.csv (21,794 samples, 18.4% positive)
- val_text.csv (1,081 samples, 22.1% positive)
- test_text.csv (1,020 samples, 24.2% positive)
- DATASET_REPORT.md (detailed statistics and construction methodology)
Total: 23,895 samples with 3,142 unique RNAs and 363 unique compounds

3. Compound Out-of-Domain Split (`compound_ood/`)

Purpose: Virtual screening of novel compounds against known RNA targets
Characteristics: Zero compound overlap, complete RNA reuse (86% of training RNAs appear in test)
Files:
- train_text.csv (31,576 samples, 21.0% positive)
- val_text.csv (6,694 samples, 20.1% positive)
- test_text.csv (6,779 samples, 15.2% positive)
- DATASET_REPORT.md (detailed statistics and construction methodology)
Total: 45,049 samples with 3,258 unique RNAs and 345 unique compounds

Data Format

Each CSV file contains the following columns:

rna_id: RNA identifier
compound_id: Compound identifier (PubChem CID)
rna_sequence: RNA nucleotide sequence (A, U, G, C)
compound_smiles: SMILES string representation of the compound
label: Binary interaction label (1 = interaction, 0 = no interaction)

Scripts

The scripts/ folder contains:

create_disjoint_splits.py: Script used to generate the out-of-domain splits with strict entity disjointness

Data Sources

RNAs: Mapped from NCBI, Ensembl, miRBase, and circBase
Compounds: Mapped from PubChem
Interactions: Curated from RNAInter repository with experimental evidence

Citation

If you use this dataset in your research, please cite:

@article{drugrna2025,
  title={LLM Agents Enable RNA–Small Molecule Interaction Prediction at Performance Comparable to Human-Designed Models},
  author={...},
  journal={...},
  year={2025}
}

License

This dataset is provided for research purposes. Please refer to the original data sources for specific licensing information.

Contact

For questions or issues, please open an issue on the GitHub repository or contact the authors.

DrugRNA Dataset