Drug-ACE: Applicability Conditions for Therapeutic Drug-Disease Relations

A text dataset for biomedical information extraction, developed for the ACL 2026 Findings paper 'Applicability Condition Extraction for Therapeutic Drug-Disease Relations'. The dataset is authored by B1tta and was last updated on June 18, 2026. It focuses on identifying context-specific conditions under which a drug is therapeutically effective for a disease.

Use Cases

• Train models to extract applicability conditions for drug-disease relations based on the described focus on context-specific factors.
• Benchmark biomedical relation extraction systems against a task that goes beyond simple binary drug-disease linking.
• Support clinical decision-making research by providing data for identifying nuanced therapeutic contexts mentioned in the description.

Strengths

• Dataset is associated with a peer-reviewed paper accepted by ACL 2026 Findings, suggesting academic validation.
• Focuses on a specific, under-addressed task of extracting applicability conditions, as described.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Created for the ACL 2026 paper 'Applicability Condition Extraction for Therapeutic Drug-Disease Relations'.

Freshness

Last updated 2026-06-18 03:20:46; freshness should be verified.