Datasets:

HydroSophyTech
/

ToxiMol-benchmark

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

🔥 News

🏆 ToxiMol has been accepted as an Oral presentation at the 32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), AI for Sciences Track.

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
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	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:

{
  "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{lin2026breakingbadmoleculesmllms,
      title={Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?}, 
      author={Fei Lin and Ziyang Gong and Cong Wang and Tengchao Zhang and Yonglin Tian and Yining Jiang and Ji Dai and Chao Guo and Xiaotong Yu and Xue Yang and Gen Luo and Fei-Yue Wang},
      year={2026},
      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.