README.md

---

license: mit
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- chemistry
- molecular-properties
- drug-discovery
- spectroscopy
- safety-assessment
- synthetic-chemistry
- rlvr
- reinforcement-learning
- cheminformatics
- rdkit
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: test
    path: test.parquet
---


# ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards

## Dataset Description

ChemBench-RLVR is a high-quality, balanced dataset containing **16,699 question-answer pairs** across **14 chemistry task types**. This dataset is specifically designed for training language models using Reinforcement Learning from Verifiable Rewards (RLVR), where all answers are computationally verifiable using established cheminformatics tools.

### Key Features

- 🧪 **16,699 balanced QA pairs** across 14 chemistry domains
- 🔬 **100% local calculations** - no external API dependencies  
- ⚖️ **Perfect task balance** - each task has exactly 1,192 samples
- 🎯 **Verifiable answers** - all responses computed using RDKit, spyrmsd, and other reliable tools
- 📚 **Template diversity** - 3 prompt variations per task
- 🌐 **Molecular diversity** - sourced from 10,000 PubChem compounds
- 📦 **Multiple formats** - Available in both Parquet and JSONL formats

## Dataset Statistics

### Overview
- **Total Samples**: 16,699
- **Training Split**: 15,029 samples (90%)
- **Test Split**: 1,670 samples (10%)
- **Generation Time**: 194.2 seconds
- **Average per Task**: 1,192 samples
- **Zero Duplicates**: All QA pairs are unique
- **Reproducible**: Fixed seed (42) for consistent results

## Task Distribution

### Complete Task Breakdown
- **Bioactivity Prediction**: 1,284 samples- **Drug Likeness Assessment**: 1,284 samples- **Functional Group Identification**: 1,275 samples- **Ghs Hazard Statement Identification**: 1,146 samples- **Ghs Pictogram Identification**: 1,144 samples- **Hydrogen Bond Properties**: 1,304 samples- **Iupac Name Generation**: 1,305 samples- **Logp Calculation**: 129 samples- **Molecular Weight Calculation**: 1,303 samples- **Molecule Visualization**: 1,305 samples- **Reactivity Prediction**: 1,305 samples- **Solubility Prediction**: 1,305 samples- **Stereochemistry Analysis**: 1,305 samples- **Synthetic Accessibility**: 1,305 samples

## Chemistry Task Categories

### 🧪 Core Molecular Properties (6 tasks)
- **Molecular Weight Calculation**: Exact molecular mass computation using RDKit
- **LogP Calculation**: Octanol-water partition coefficient prediction  
- **Aromatic Ring Count**: Identification of aromatic ring systems
- **Hydrogen Bond Properties**: Count of donors and acceptors
- **IUPAC Name Generation**: Systematic nomenclature from structure
- **Molecule Visualization**: 2D structural diagram generation

### 🔬 Advanced Spectroscopy & Structure (3 tasks)
- **NMR Signal Prediction**: 1H and 13C chemical shift estimation via RDKit fallback methods
- **Point Group Determination**: Molecular symmetry analysis using RDKit/spyrmsd
- **Stereochemistry Analysis**: Chiral center identification and stereoisomer enumeration
- **Functional Group Identification**: SMARTS-based substructure recognition

### ⚠️ Safety & Hazard Assessment (2 tasks)
- **GHS Pictogram Identification**: Hazard symbol classification from structure
- **GHS Hazard Statement Identification**: H-code assignment using chemical patterns

### 💊 Pharmaceutical Chemistry (4 tasks)
- **Drug-Likeness Assessment**: Lipinski's Rule of Five evaluation
- **Solubility Prediction**: Aqueous solubility estimation via group contribution
- **Bioactivity Prediction**: Pharmacological class prediction from structural features
- **Stereochemistry Analysis**: Chiral center identification and stereoisomer counting

### ⚗️ Synthetic Chemistry (2 tasks)
- **Synthetic Accessibility**: Complexity scoring for synthesis planning
- **Reactivity Prediction**: Reactive site identification and charge analysis

## Task Distribution

- **Bioactivity Prediction**: 1,284 samples
- **Drug Likeness Assessment**: 1,284 samples
- **Functional Group Identification**: 1,275 samples
- **Ghs Hazard Statement Identification**: 1,146 samples
- **Ghs Pictogram Identification**: 1,144 samples
- **Hydrogen Bond Properties**: 1,304 samples
- **Iupac Name Generation**: 1,305 samples
- **Logp Calculation**: 129 samples
- **Molecular Weight Calculation**: 1,303 samples
- **Molecule Visualization**: 1,305 samples
- **Reactivity Prediction**: 1,305 samples
- **Solubility Prediction**: 1,305 samples
- **Stereochemistry Analysis**: 1,305 samples
- **Synthetic Accessibility**: 1,305 samples

## Dataset Structure

Each sample contains:
- **messages**: List of conversation turns (user question, assistant answer)
- **task**: Chemistry task category
- **smiles**: SMILES string of the molecule
- **difficulty**: Task difficulty level (easy/medium/hard)

### Example Sample

```json

{

  "messages": [

    {

      "role": "user", 

      "content": "What is the molecular weight of the compound with SMILES 'CCO'?"

    },

    {

      "role": "assistant",

      "content": "The molecular weight of ethanol (CCO) is 46.07 g/mol."

    }

  ],

  "task": "Molecular_Weight_Calculation",

  "smiles": "CCO",

  "difficulty": "easy"

}

```

## Computational Methods

All answers are computed using established cheminformatics libraries:

- **RDKit**: Molecular property calculations, structure analysis
- **spyrmsd**: Symmetry-corrected molecular analysis  
- **MDAnalysis**: Molecular dynamics and structure processing
- **PyTorch**: Neural network components (when available)

## Usage

### Loading the Dataset

```python

from datasets import load_dataset



# Load full dataset

dataset = load_dataset("summykai/chembench-rlvr-test5")



# Load specific split

train_data = load_dataset("summykai/chembench-rlvr-test5", split="train")

test_data = load_dataset("summykai/chembench-rlvr-test5", split="test")

```

### RLVR Training

This dataset is optimized for Reinforcement Learning from Verifiable Rewards:

```python

# Example: Verify molecular weight calculation

from rdkit import Chem

from rdkit.Chem import Descriptors



def verify_molecular_weight(smiles: str, predicted_mw: float) -> bool:

    mol = Chem.MolFromSmiles(smiles)

    if mol is None:

        return False

    actual_mw = Descriptors.MolWt(mol)

    return abs(actual_mw - predicted_mw) < 0.1

```

## Citation

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

```bibtex

@dataset{chembench_rlvr_2025,

  title={ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards},

  author={ChemBench Team},

  year={2025},

  url={https://huggingface.co/datasets/summykai/chembench-rlvr-test5},

  note={Generated using RDKit, spyrmsd, and other open-source cheminformatics tools}

}

```

## License

This dataset is released under the MIT License. See LICENSE file for details.

## Dataset Generation

- **Generated on**: 2025-08-08 19:06:47 UTC
- **Version**: 8.6-post8
- **Seed**: 42 (for reproducibility)
- **Source molecules**: PubChem compound database

## Acknowledgments

This dataset was generated using:
- [RDKit](https://www.rdkit.org/) - Cheminformatics toolkit
- [spyrmsd](https://github.com/RMeli/spyrmsd) - Symmetry-corrected RMSD calculations
- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) - Chemical compound database
- [Hugging Face](https://huggingface.co/) - Dataset hosting and distribution