MSER-DDI: molecular and semantic event representations for few-shot and unseen-event drug–drug interaction prediction
Year: 2026
Type: article
Abstract: Introduction Sparse event annotations limit drug-drug interaction (DDI) prediction, particularly for rare events and event categories absent from training data. Methods We propose MSER-DDI, a framework with two independently trained pathways that represent target events using labeled drug pairs or textual descriptions. The sample-driven pathway, PharDDIE, combines molecular structure and biological context with reconstruction-regularized latent encoding to construct event prototypes from a few labeled pairs. The semantics-driven pathway, EviDDIE, maps event descriptions into the drug-pair representation space and uses evidential inference without labeled target-event examples. Results In 1-shot rare-event prediction, PharDDIE achieved higher mean area under the receiver operating characteristic curve (AUROC), accuracy, and event-macro F1 than retrained RareDDIE across five matched training seeds. These means also exceeded the published baseline estimates. Under the respective evaluation conditions, 5-shot predictions had lower Brier score, negative log-likelihood, and calibration error than 1-shot predictions, together with lower high-confidence error rates and broader coverage across all three event-frequency groups. EviDDIE predicted interactions for event categories excluded from molecular training. Literature-based assessment identified drug- or class-level pharmacological consistency for a subset of selected positive benchmark examples. Discussion MSER-DDI supports event-level prediction when target-event annotations are scarce or unavailable, while joint evaluation of discrimination and probability quality characterizes predictive performance and confidence. The resulting predictions provide interaction hypotheses for further pharmacological investigation.
Source: Frontiers in Chemistry
Institutions: Hunan First Normal University, Changsha University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225375979