RxPairEvid: an auditable, machine-learning-ready dataset of drug–drug pairs with pharmacovigilance signal features and MedDRA PT-code rationales

<ns3:p>Background Drug–drug interactions (DDIs) remain a major source of preventable harm, yet many computational DDI resources are hard to reproduce, difficult to audit, or constrained by redistribution licenses. We created RxPairEvid-50 K to provide a small, license-clean, model-ready matrix of canonical drug pairs with conservative pharmacovigilance signal summaries and a rationale pointer that supports human interpretation. Methods RxPairEvid is derived from the FDA Adverse Event Reporting System (FAERS) by resolving drug mentions to a stable 14-character InChIKey stem (IK14), enumerating co-medication pairs per case, joining outcomes at the MedDRA Preferred Term (PT) code level, and computing disproportionality statistics (PRR, ROR, and the continuity-corrected lower 95% confidence bound of ROR) for each pair–PT. Signals are rolled up to per-pair features under strict count floors (a_raw≥3, pair≥10, PT ≥ 10). RxPairEvid-50 K is a deterministic stratified sample from the strict matrix. Conclusions RxPairEvid-50 K contains 50,000 drug–drug pairs with stable identifiers, strict-regime FAERS signal features, PT-code rationale pointers, and audit artifacts. It is intended to support benchmarking, label construction, and exploratory modeling of interaction risk with transparent, reproducible processing steps.</ns3:p>