metadata

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: 560
  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

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

560 representative toxic molecules spanning diverse toxicity mechanisms and varying granularities
11 primary toxicity repair tasks based on Therapeutics Data Commons (TDC) platform
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:

Identify potential toxicity endpoints from molecular structures
Interpret semantic constraints from natural language descriptions
Generate structurally similar substitute molecules that eliminate toxic fragments
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	50	Mutagenicity testing
Carcinogens	Binary Classification	50	Carcinogenicity prediction
ClinTox	Binary Classification	50	Clinical toxicity data
DILI	Binary Classification	50	Drug-induced liver injury
hERG	Binary Classification	50	hERG channel inhibition
hERG_Central	Binary Classification	50	Large-scale hERG database with integrated cardiac safety profiles
hERG_Karim	Binary Classification	50	hERG data from Karim et al.
LD50_Zhu	Regression (log(LD50) < 2)	50	Acute toxicity
Skin_Reaction	Binary Classification	50	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)	50	Diverse toxicity pathways including mitochondrial dysfunction, immunosuppression, and neurotoxicity

Data Structure

Each entry contains:

{
  "task": "string",      // Toxicity task identifier
  "id": "int",           // Molecule ID
  "smiles": "string",    // SMILES representation
  "image": "binary" // 2D molecular structure image binary
}

Available Subdatasets

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:

@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 file for details.