{
  "abstract": "Network pharmacology examines drug action across interacting targets, pathways, and disease modules. Increasingly, however, the graph neural networks and knowledge-graph models used for this purpose are difficult to inspect. This critical narrative review considers feature attribution, attention, counterfactual and rule-based explanation, explainable graph neural networks, Bayesian and causal methods, and path-based knowledge-graph reasoning. We assess these approaches against three demands imposed by biological graphs: preservation of heterogeneous node and relation semantics, support for multi-hop reasoning, and concordance with evidence excluded from model training. Among the methods reviewed, none has yet been shown to satisfy all three demands consistently. A model-faithful explanation may still emphasize node degree, network hubs, or heavily studied entities instead of a disease-specific mechanism. To address this problem, we propose five evaluation domains: faithfulness, stability, external biological concordance, bias sensitivity, and translational utility, together with a six-item reporting standard. Both are conceptual proposals; they remain unweighted, uncalibrated, and unevaluated empirically. An exploratory, literature-based application to TxGNN, DDI-GCN, and MINERVA shows that transparency-related reporting is useful but incomplete. This exercise demonstrates how the criteria might be used; it is not an empirical evaluation or calibration of the framework. We did not retrain or rerun any model or conduct new quantitative benchmarking. The field still lacks a unified biomedical benchmark that evaluates prediction and explanation together under heterogeneous, cold-start, and provenance-aware graph settings. Progress will depend on task-matched evaluation, explicit treatment of uncertainty, and experiments designed to distinguish what a model uses from what biology actually does.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia",
    "Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Khaled S. Allemailem",
      "orcid": "0000-0002-6486-9835"
    },
    {
      "affs": [
        0
      ],
      "name": "Ahmad Almatroudi",
      "orcid": "0000-0002-1491-6402"
    },
    {
      "affs": [
        1
      ],
      "name": "Abdullah M. Alnajim",
      "orcid": "0000-0003-3223-1105"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/07r2cgt40f2xw.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27219479",
  "fulltext_url": null,
  "id": "07r2cgt40f2xw",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/21/9479",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Explainable Artificial Intelligence (XAI)"
    ]
  ],
  "openalex": "W7223896911",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/07r2cgt40f2xw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:57Z",
      "native_id": "10.3390/ijms27219479",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/21/9479"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:1422-0067",
      "subfield": "general"
    }
  ],
  "title": "Explainable AI for Network Pharmacology: A Critical Framework for Interpreting Multi-Target Drug Discovery on Biological Graphs",
  "title_text": "Explainable AI for Network Pharmacology: A Critical Framework for Interpreting Multi-Target Drug Discovery on Biological Graphs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:57Z",
  "url": "https://research.draconitic.com/paper/07r2cgt40f2xw",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}