---
license: mit
task_categories:
- tabular-classification
- tabular-regression
task_ids:
- multi-class-classification
- tabular-single-column-regression
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
language:
- en
tags:
- chemistry
- toxicity
- molecular-design
- SMILES
- drug-discovery
- benchmark
- multimodal
- structure-activity-relationship
pretty_name: "ToxiMol: A Benchmark for Structure-Level Molecular Detoxification"
dataset_info:
  features:
    - name: task
      dtype: string
    - name: id
      dtype: int64
    - name: smiles
      dtype: string
    - name: image_path
      dtype: string
  splits:
    - name: test
      num_examples: 660
  configs:
    - config_name: ames
      data_files: "ames/*"
    - config_name: carcinogens_lagunin
      data_files: "carcinogens_lagunin/*"
    - config_name: clintox
      data_files: "clintox/*"
    - config_name: dili
      data_files: "dili/*"
    - config_name: herg
      data_files: "herg/*"
    - config_name: herg_central
      data_files: "herg_central/*"
    - config_name: herg_karim
      data_files: "herg_karim/*"
    - config_name: ld50_zhu
      data_files: "ld50_zhu/*"
    - config_name: skin_reaction
      data_files: "skin_reaction/*"
    - config_name: tox21
      data_files: "tox21/*"
    - config_name: toxcast
      data_files: "toxcast/*"
---

# ToxiMol: A Benchmark for Structure-Level Molecular Detoxification

[![arXiv](https://img.shields.io/badge/arXiv-paper-red.svg)](https://arxiv.org/pdf/2506.10912)
[![Dataset](https://img.shields.io/badge/🤗%20Hugging%20Face-ToxiMol-blue)](https://huggingface.co/datasets/DeepYoke/ToxiMol-benchmark)

## Overview

**ToxiMol** is the first comprehensive benchmark for **molecular toxicity repair** tailored to general-purpose **Multimodal Large Language Models (MLLMs)**. This is the dataset repository for the paper "Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?".

## Key Features

### 🧬 Comprehensive Dataset
- **660 representative toxic molecules** spanning diverse toxicity mechanisms and varying granularities
- **11 primary toxicity repair tasks** based on [Therapeutics Data Commons (TDC) platform](https://tdcommons.ai/single_pred_tasks/tox/)
- **Multi-granular coverage**: Tox21 (12 sub-tasks), ToxCast (10 sub-tasks), and 9 additional datasets
- **Multimodal inputs**: SMILES strings + 2D molecular structure images rendered using RDKit

### 🎯 Challenging Task Definition
The molecular toxicity repair task requires models to:
1. **Identify potential toxicity endpoints** from molecular structures
2. **Interpret semantic constraints** from natural language descriptions
3. **Generate structurally similar substitute molecules** that eliminate toxic fragments
4. **Satisfy drug-likeness and synthetic feasibility** requirements

### 📊 Systematic Evaluation
- **ToxiEval framework**: Automated evaluation integrating toxicity prediction, synthetic accessibility, drug-likeness, and structural similarity
- **Comprehensive analysis**: Evaluation of ~30 mainstream MLLMs with ablation studies
- **Multi-dimensional metrics**: Success rate analysis across different evaluation criteria and failure modes

## Dataset Structure

### Task Overview
| Dataset | Task Type | # Molecules | Description |
|---------|-----------|-------------|-------------|
| **AMES** | Binary Classification | 60 | Mutagenicity testing |
| **Carcinogens** | Binary Classification | 60 | Carcinogenicity prediction |
| **ClinTox** | Binary Classification | 60 | Clinical toxicity data |
| **DILI** | Binary Classification | 60 | Drug-induced liver injury |
| **hERG** | Binary Classification | 60 | hERG channel inhibition |
| **hERG_Central** | Binary Classification | 60 | Large-scale hERG database with integrated cardiac safety profiles |
| **hERG_Karim** | Binary Classification | 60 | hERG data from Karim et al. |
| **LD50_Zhu** | Regression (log(LD50) < 2) | 60 | Acute toxicity |
| **Skin_Reaction** | Binary Classification | 60 | Adverse skin reactions |
| **Tox21** | Binary Classification (12 sub-tasks) | 60 | Nuclear receptors, stress response pathways, and cellular toxicity mechanisms (ARE, p53, ER, AR, etc.) |
| **ToxCast** | Binary Classification (10 sub-tasks) | 60 | Diverse toxicity pathways including mitochondrial dysfunction, immunosuppression, and neurotoxicity |

### Data Structure
Each entry contains:
```json
{
  "task": "string",      // Toxicity task identifier
  "id": "int",           // Molecule ID
  "smiles": "string",    // SMILES representation
  "image": "binary" // 2D molecular structure image binary
}
```

<!-- ## Usage -->
<!-- 
### Loading the Dataset

```python
from datasets import load_dataset

# Load a specific subdataset
ames_dataset = load_dataset("DeepYoke/ToxiMol-benchmark", "ames")
tox21_dataset = load_dataset("DeepYoke/ToxiMol-benchmark", "tox21")

# Access the data
for example in ames_dataset['test']:
    print(f"Task: {example['task']}")
    print(f"ID: {example['id']}")
    print(f"SMILES: {example['smiles']}")
    print(f"Image: {example['image_path']}")
    print("-" * 50)
``` -->

## Available Subdatasets
```python
subdatasets = [
    "ames", "carcinogens_lagunin", "clintox", "dili", 
    "herg", "herg_central", "herg_karim", "ld50_zhu", 
    "skin_reaction", "tox21", "toxcast"
]

# Load all datasets
datasets = {}
for name in subdatasets:
    datasets[name] = load_dataset("DeepYoke/ToxiMol-benchmark", data_dir=name)
```

## Experimental Results

Our systematic evaluation of ~30 mainstream MLLMs reveals:
- **Current limitations**: Overall success rates remain relatively low across models
- **Emerging capabilities**: Models demonstrate initial potential in toxicity understanding, semantic constraint adherence, and structure-aware molecule editing
- **Key challenges**: Structural validity, multi-dimensional constraint satisfaction, and failure mode attribution

## Citation

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

```bibtex
@misc{lin2025breakingbadmoleculesmllms,
      title={Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?}, 
      author={Fei Lin and Ziyang Gong and Cong Wang and Yonglin Tian and Tengchao Zhang and Xue Yang and Gen Luo and Fei-Yue Wang},
      year={2025},
      eprint={2506.10912},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.10912}, 
}
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.