CAFNet-DG: Task-Specific Modeling and Prevalence-Aware Evaluation for Adverse Drug Reaction Prioritization with Ordinal-Frequency Estimation.

Authors

Wen Li, Zhiyu Xu, Xiangqiong Wu, Ju Xiang, Xiaojie Zhang

Published in

International journal of molecular sciences. Volume 27. Issue 19. Sep 28, 2026. Epub Sep 28, 2026.

Abstract

Adverse drug reaction (ADR) prioritization and ordinal-frequency estimation are related but distinct prediction objectives, while aggregate ranking performance in sparse annotation matrices can be strongly influenced by frequently annotated side effects. Within CAFNet-DG, CAFNet-D uses task-specific association and ordinal-frequency functions built on shared molecular-relational representations, while annotation prevalence is evaluated explicitly within each training fold. On a SIDER-derived dataset comprising 750 drugs and 994 side effects, replacing a shared output with task-specific functions produced the largest joint change in staged drug-disjoint ablation, increasing mAP from 0.340 to 0.382 and reducing RMSE from 1.754 to 1.142. A prevalence-only ranking achieved an mAP of 0.410, close to the CAFNet-DG value of 0.413, indicating that aggregate ADR ranking is strongly associated with the benchmark annotation distribution. Prevalence-matched, prevalence-stratified, frequently annotated side-effect removal, and per-drug analyses were therefore used to distinguish drug-conditioned ranking variation from global annotation-prevalence effects. An equal-weight same-architecture ensemble control yielded no significant differences from CAFNet-DG across seven ranking metrics after Holm correction; generic variance reduction therefore could not be excluded as an explanation for the modest score-integration gain. Scaffold-disjoint and independent-data evaluations showed modest transfer, while ordinal-frequency agreement on CT-ADE remained limited. The results support the CAFNet-D task-specific formulation and prevalence-aware evaluation, while CAFNet-DG provides the score-integration setting examined under matched ensemble controls.

PMID:
42883930
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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