
HODC-AES is a reliability-aware benchmark for higher-order drug-combination adverse-event signal prediction in computational pharmacovigilance. This v1.0.0 release contains the full archival benchmark package, including processed benchmark artifacts, generated contrastive negatives, prediction outputs, paper-ready tables and figures, documentation, release metadata, and SHA256 checksums. The benchmark is built from public FAERS quarterly reports and is designed for evaluation under random and temporal validation, hard-negative protocols, calibration analysis, top-k alert-budget evaluation, and temporal generalization-gap analysis. Main release statistics:- 973,898 positive signal candidates- 1,500,000 generated contrastive negatives- Five negative protocols: random, drug-count-matched, frequency-matched, one-drug-replacement, and hybrid-hard- Zero known-positive overlap among generated negatives- 165,878 unique drug combinations- 12,311 unique reaction terms Important: HODC-AES treats FAERS-derived labels as reported pharmacovigilance signal candidates, not as confirmed causal clinical drug-drug interactions. The resource is intended for research use only and not for clinical decision-making.
drug safety, machine learning, benchmark dataset, FAERS, computational pharmacovigilance, higher-order drug combinations, top-k evaluation, adverse-event signal prediction, temporal validation, calibration
drug safety, machine learning, benchmark dataset, FAERS, computational pharmacovigilance, higher-order drug combinations, top-k evaluation, adverse-event signal prediction, temporal validation, calibration
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