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CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning

Y. Cheung

Abstract

Combination pharmacotherapy offers substantial therapeutic advantages but also poses substantial risks of adverse drug reactions (ADRs). The accurate prediction of ADRs with interpretable computational methods is crucial for clinical safety management, drug development, and precision medicine. However, managing ADRs remains a challenge due to the vast search space of drug combinations and the complexity of physiological responses. Current graph-based architectures often struggle to effectively integrate multi-scale biological information and frequently rely on fixed association matrices, which limits their ability to capture dynamic organ-level dependencies and generalize across diverse datasets. Here we propose CrossADR, a hierarchical framework for organ-level ADR prediction through cross-layer feature integration and cross-level associative learning. It incorporates a gated-residual-flow graph neural network to fuse multi-scale molecular features and utilizes a learnable ADR embedding space to dynamically capture latent biological correlations across 15 organ systems. Systematic evaluation on the newly constructed CrossADR-Dataset-covering 1,376 drugs and 946,000 unique combinations-demonstrates that CrossADR consistently achieves state-of-the-art performance across 80 distinct experimental scenarios and provides high-resolution insights into drug-related protein protein interactions and pathways. Overall, CrossADR represents a robust tool for cross-scale biomedical information integration, cross-layer feature integration as well as cross-level associative learning, and can be effectively utilized to prevent ADRs in clinical decision-making.

CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning

Abstract

Combination pharmacotherapy offers substantial therapeutic advantages but also poses substantial risks of adverse drug reactions (ADRs). The accurate prediction of ADRs with interpretable computational methods is crucial for clinical safety management, drug development, and precision medicine. However, managing ADRs remains a challenge due to the vast search space of drug combinations and the complexity of physiological responses. Current graph-based architectures often struggle to effectively integrate multi-scale biological information and frequently rely on fixed association matrices, which limits their ability to capture dynamic organ-level dependencies and generalize across diverse datasets. Here we propose CrossADR, a hierarchical framework for organ-level ADR prediction through cross-layer feature integration and cross-level associative learning. It incorporates a gated-residual-flow graph neural network to fuse multi-scale molecular features and utilizes a learnable ADR embedding space to dynamically capture latent biological correlations across 15 organ systems. Systematic evaluation on the newly constructed CrossADR-Dataset-covering 1,376 drugs and 946,000 unique combinations-demonstrates that CrossADR consistently achieves state-of-the-art performance across 80 distinct experimental scenarios and provides high-resolution insights into drug-related protein protein interactions and pathways. Overall, CrossADR represents a robust tool for cross-scale biomedical information integration, cross-layer feature integration as well as cross-level associative learning, and can be effectively utilized to prevent ADRs in clinical decision-making.
Paper Structure (27 sections, 34 equations, 8 figures, 5 tables)

This paper contains 27 sections, 34 equations, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Problem formulation of ADR prediction
  4. Model: architecture of CrossADR
  5. Molecular feature
  6. Knowledge graph
  7. GNN module: Cross-layer GNN on KG
  8. Cross-level attention module: Learnable space for ADR Embeddings
  9. Dataset: raw datasets and construction of CrossADR-Dataset
  10. Raw datasets
  11. Construction of CrossADR-Dataset
  12. Experiment: train and evaluate CrossADR
  13. Loss function
  14. Optimizer
  15. Evaluation metrics
  16. ...and 12 more sections

Figures (8)

  • Figure 1: Overview of the CrossADR framework for organ-level adverse drug reaction (ADR) prediction. (A) Input: drug pairs, as well as the 15 ADR labels at organ level. (B) The training and application workflow of CrossADR for predicting ADRs and performing downstream PPI network and pathway analysis. (C) Architectural comparison between baseline methods (GNN-KG-based, OrganADR) and the proposed CrossADR model, highlighting two proposed important module: (1) the cross-layer feature integration module, (2) the learnable ADR embeddings as well as cross-level gated module and attention mechanisms.
  • Figure 2: Detailed architecture of CrossADR. (A) Task setting for "emerging drug -emerging drug" ADR prediction task. (B) The cross-layer GNN on KG module. (C) The workflow of CrossADR. (D) Molecular feature. (E) The cross-level gated module and attention module with learnable ADR embedding space.
  • Figure 3: Knowledge Graph (KG) construction and ablated KGs. (A) Integration of TWOSIDES and ADReCS, as well as the topology of the basic KG. (B,C,D) Three ablated KGs for comparison. B, C and D for ablated KG 1, 2 and 3. Based on the basic KG, different type of information is removed accordingly.
  • Figure 4: Data annotation and sample generation for the CrossADR-Dataset. (A) Mapping drug combinations from TWOSIDES and ADRECS to 15 specific organs to create multi organ ADR labels. (B) Integration of synergistic and ADR-related information from DrugBank to define positive and negative samples.
  • Figure 5: Construction and statistics of the CrossADR-Dataset. (A) Determine the drugs to be analyzed for CrossADR. (B) Construction of one sample (one drug combination and its annotations); definition of positive sample and negative sample. (C) CrossADR database, CrossADR-Dataset D and CrossADR-Dataset R. (D, E) Comparison of the number of drugs, total combination space and other metrics between OrganADR-Dataset and the proposed CrossADR-Dataset.
  • ...and 3 more figures