What does this research mean for the field?

Integrating Explainable Artificial Intelligence (XAI) into AI-based drug-drug interaction prediction models is necessary to ensure safety, transparency, and clinical reliability in polypharmacy decision-making. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review explainable artificial intelligence methodologies and heterogeneous graph structures applied to drug–drug interaction prediction to improve model transparency and clinical reliability.

September 15, 2026Iraqi Journal for Computers and InformaticsOpen Access

Review on Explainable Modeling of Drug–Drug Interactions Using Heterogeneous Graph Structures

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Authors

EZEsraa Sameer Zbari SSSalah Saleh

Discussion

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Overview

Review highlights explainable artificial intelligence frameworks for drug-drug interaction prediction in polypharmacy, suggesting interpretable graphs enhance clinical decision-making safety.

Key Points

To review explainable artificial intelligence methodologies and heterogeneous graph structures applied to drug–drug interaction prediction to improve model transparency and clinical reliability.
Synthesized public databases, clinical resources, and benchmark datasets used in computational drug–drug interaction research.
Categorized data handling, feature preprocessing pipelines, and heterogeneous graph-based artificial intelligence architectures.
Examined explainable artificial intelligence frameworks tailored for interpreting predictive outcomes in multi-drug therapy regimens.
Showed that black-box artificial intelligence architectures restrict clinical adoption due to opaque decision logic in critical polypharmacy scenarios.
Demonstrated that heterogeneous graph representations paired with explainable artificial intelligence clarify biological relationship pathways and feature importance.
Identified critical bottlenecks in current interpretability validation metrics and outlined future requirements for safe clinical deployment.

Cite This Study

Zbari et al. (2026) studied this question.

synapsesocial.com/papers/6aa9132f9013453be30a0eef https://doi.org/10.25195/ijci.v52i2.759

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September 15, 2026Iraqi Journal for Computers and InformaticsOpen Access

Review on Explainable Modeling of Drug–Drug Interactions Using Heterogeneous Graph Structures

View Full Paper

Ask AI

Bookmark

Authors

EZEsraa Sameer Zbari SSSalah Saleh

Discussion

Member takes

Overview

Review highlights explainable artificial intelligence frameworks for drug-drug interaction prediction in polypharmacy, suggesting interpretable graphs enhance clinical decision-making safety.

Key Points

To review explainable artificial intelligence methodologies and heterogeneous graph structures applied to drug–drug interaction prediction to improve model transparency and clinical reliability.
Synthesized public databases, clinical resources, and benchmark datasets used in computational drug–drug interaction research.
Categorized data handling, feature preprocessing pipelines, and heterogeneous graph-based artificial intelligence architectures.
Examined explainable artificial intelligence frameworks tailored for interpreting predictive outcomes in multi-drug therapy regimens.
Showed that black-box artificial intelligence architectures restrict clinical adoption due to opaque decision logic in critical polypharmacy scenarios.
Demonstrated that heterogeneous graph representations paired with explainable artificial intelligence clarify biological relationship pathways and feature importance.
Identified critical bottlenecks in current interpretability validation metrics and outlined future requirements for safe clinical deployment.

Cite This Study

Zbari et al. (2026) studied this question.

synapsesocial.com/papers/6aa9132f9013453be30a0eef https://doi.org/10.25195/ijci.v52i2.759

View Full Paper

Ask AI

Bookmark