---
license: cc-by-4.0
task_categories:
- text-classification
- tabular-classification
- zero-shot-classification
- text-generation
size_categories:
- 10K<n<100K
language:
- en
tags:
- chemistry
- molecular-properties
- hazard-prediction
- GHS
- safety
- cheminformatics
- llm-evaluation
- multi-label-classification
---

# Dataset Card for SafeChem

## Dataset Summary

SafeChem is a regulatory-grounded benchmark dataset of 32,211
chemical substances for multi-label GHS (Globally Harmonized System)
hazard prediction and LLM safety reliability evaluation. Unlike prior
molecular benchmarks constructed by querying pharmaceutical databases,
SafeChem is seeded from a curated hazardous materials registry,
ensuring coverage of real-world industrial and safety-critical chemicals
including solvents, reactive gases, agrochemicals, and heavy metal
compounds underrepresented in drug-focused benchmarks.

Each substance is annotated with molecular structure representations
(canonical SMILES, InChI, InChI key), eight physicochemical descriptors,
free-text physical descriptions, and 30 binary GHS hazard classification
labels spanning toxicological, physical, and environmental hazard
categories derived from European regulatory sources (CLP Regulation and
ECHA C&L Inventory as aggregated by PubChem).

The dataset supports two benchmark tasks:
- **Task 1 — Multi-label hazard prediction**: Scaffold-split evaluation
  of GHS hazard classification from molecular structure across 20 retained
  hazard classes with sufficient test-set support.
- **Task 2 — LLM safety hallucination benchmark**: Evaluation of omission
  hallucination rate, commission hallucination, and silent abstention
  behavior across LLMs on a curated 500-substance high-hazard subset.

## Dataset Details

### Dataset Description

- **Curated by:** Anonymous (camera-ready version will include full
  author and institution information)
- **License:** CC BY 4.0
- **Language:** English
- **Size:** 32,211 unique substances (deduplicated by PubChem CID)

### Dataset Sources

- **Primary source:** Hazardous Materials Management Information System
  (HMMIS) regulatory registry (anonymized for double-blind review)
- **Molecular enrichment:** PubChem Compound and GHS Classification
  databases (https://pubchem.ncbi.nlm.nih.gov)
- **GHS annotations:** ECHA CLP Regulation submissions and C&L Inventory,
  Japan NITE, and other national regulatory bodies as aggregated by PubChem

## Dataset Structure

### Files

| File | Description | Size |
|---|---|---|
| `full_dataset.csv` | Complete 32,211-substance master file with all 57 columns | 32,614 rows |
| `split_train.csv` | Training partition (70%, scaffold split) | ~22,532 rows |
| `split_val.csv` | Validation partition (15%, scaffold split) | ~4,847 rows |
| `split_test.csv` | Test partition (15%, scaffold split) | ~4,832 rows |

### Column Groups

**Molecular identifiers**
- `cas_number`, `cid`, `inchi`, `inchi_key`, `canonical_smiles`,
  `isomeric_smiles`, `iupac_name`, `molecular_formula`

**Molecular descriptors** (pre-computed via RDKit and PubChem)
- `molecular_weight`, `xlogp`, `tpsa`, `complexity`,
  `h_bond_donor_count`, `h_bond_acceptor_count`,
  `rotatable_bond_count`, `heavy_atom_count`

**Physical properties**
- `boiling_point`, `flash_point`, `physical_description`

**GHS hazard labels** (30 binary columns, 1 = hazard applies, 0 = not classified)
- Health: `acute_toxicity_oral`, `acute_toxicity_dermal`,
  `acute_toxicity_inhalation`, `carcinogenicity`,
  `germ_cell_mutagenicity`, `reproductive_toxicity`,
  `skin_corrosion_irritation`, `skin_sensitization`,
  `eye_damage_irritation`, `respiratory_sensitization`,
  `aspiration_hazard`, `stot_single_exposure`,
  `stot_repeated_exposure`
- Physical: `flammable_liquids`, `flammable_gases`, `flammable_solids`,
  `flammable_aerosols`, `explosives`, `oxidizing_liquids`,
  `oxidizing_solids`, `oxidizing_gases`, `organic_peroxides`,
  `self_reactive_substances`, `pyrophoric_substances`,
  `self_heating_substances`, `water_reactive_substances`,
  `gases_under_pressure`, `metal_corrosion`
- Environmental: `acute_aquatic_toxicity`, `chronic_aquatic_toxicity`

### Dataset Splits

Splits were generated using **Bemis-Murcko scaffold splitting** to
prevent structural data leakage between train and test, combined with
**iterative stratification** to preserve per-label positive rates across
partitions. This is the standard evaluation protocol for molecular ML
benchmarks. A 70/15/15 train/validation/test ratio was applied.

**Task 1 benchmark uses 20 of the 30 labels** — those with at least 10
positive instances in the test partition after scaffold splitting. The
remaining 10 labels are retained in the full dataset and used in Task 2.

## Dataset Creation

### Curation Rationale

Existing molecular ML benchmarks (MoleculeNet, Tox21) are populated
primarily from pharmaceutical and bioassay databases, biasing their
chemical space toward drug-like molecules. Real-world chemical safety
workflows involve industrial solvents, reactive gases, agrochemicals,
and heavy metal compounds that are systematically underrepresented in
these benchmarks. SafeChem was created to fill this gap by
grounding the substance population in a regulatory hazardous materials
registry rather than database availability.

### Data Collection and Processing

1. **Seed corpus**: Substance identifiers (CAS numbers) were extracted
   from a regulatory hazardous materials management information system
   (HMMIS).
2. **PubChem enrichment**: CAS numbers were resolved to PubChem CIDs
   via the PubChem identifier exchange service. Records were retained
   only if they had a valid canonical SMILES and at least one GHS
   hazard classification entry.
3. **Descriptor retrieval**: Molecular descriptors were retrieved from
   PubChem's computed properties endpoint and cross-validated against
   RDKit recomputation; discrepancies exceeding 5% used the RDKit value.
4. **Deduplication**: Records were deduplicated by PubChem CID. 
5. **Filtering**: Substances with no positive GHS labels were excluded.
   The final corpus contains 32,211 unique substances.

### Source Data Producers

GHS hazard annotations are regulatory submissions from government
bodies and industry to ECHA, NITE, and other national authorities,
as aggregated by PubChem. Molecular structure data is sourced from
PubChem's Compound database.

### Annotations

GHS hazard labels are binary indicators derived from regulatory
classification submissions rather than manual annotation by the dataset
authors. Zero values indicate the absence of a positive GHS
classification in PubChem's aggregated records; this predominantly
reflects a regulatory determination that the hazard does not apply,
though absence of data cannot be fully excluded for all substances.

### Personal and Sensitive Information

The dataset contains only chemical substance data (molecular structures,
physicochemical properties, regulatory classifications). No personal or
sensitive information about individuals is included.

## Bias, Risks, and Limitations

- **Label noise**: A subset of annotations derives from industry
  self-notifications in the ECHA C&L Inventory, which are self-reported
  and may exhibit inter-submitter inconsistency not fully resolved by
  union merging.
- **Single compounds only**: All substances are single compounds;
  mixture toxicology is not captured.
- **Temporal snapshot**: Labels reflect the state of regulatory
  knowledge at the time of PubChem data collection and may not reflect
  subsequent regulatory updates.



## Glossary

- **GHS**: Globally Harmonized System of Classification and Labelling
  of Chemicals — the international standard for chemical hazard
  communication.
- **CLP Regulation**: EU Regulation (EC) No 1272/2008 on classification,
  labelling and packaging of substances and mixtures, which implements
  GHS in the European Union.
- **ECHA C&L Inventory**: European Chemicals Agency Classification and
  Labelling Inventory — a database of GHS classifications notified by
  industry.
- **Scaffold split**: A dataset partitioning strategy that separates
  molecules by their Bemis-Murcko core ring system, preventing
  structurally similar molecules from appearing in both train and test.
- **OHR (Omission Hallucination Rate)**: The fraction of true positive
  hazard labels that an LLM fails to predict — the safety-critical
  false-negative rate.
- **EPR (Empty Prediction Rate)**: The fraction of LLM responses that
  return no hazard labels, indistinguishable from a confirmed negative
  classification in automated pipelines.
- **SMILES**: Simplified Molecular Input Line Entry System — a string
  notation for representing molecular structures.