What Molecular Structure Cannot Tell Us: A Taxonomy of Explainability Gaps in GNN-Based Drug Toxicity Prediction
arXiv:2605.26183v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a structurally natural approach for molecular toxicity prediction, operating directly on atomic connectivity without the information loss inherent to fixed-length fingerprints. However, the fraction of a drug's known pharmacological profile that is actually encodable in its molecular structure remains systematically underexplored. This study addresses this question through a systematic case study using acetylsalicylic acid (ASA, Aspirin) - one of the most comprehensively characterized drugs in pharma
The proliferation of GNNs in drug discovery necessitates a deeper understanding of their limitations and interpretability, especially as AI adoption scales in pharmaceutical research.
Understanding the 'explainability gaps' in AI-driven drug toxicity prediction is crucial for regulatory approval, patient safety, and efficient R&D in pharmaceuticals.
This research highlights that molecular structure alone may be insufficient for comprehensive toxicity prediction, pushing for more holistic data integration beyond traditional chemical fingerprints.
- · AI companies focusing on multimodal data integration
- · Pharmaceuticals leveraging AI for early-stage drug discovery
- · Computational biology researchers
- · Companies relying solely on GNNs and molecular structure for drug toxicity predi
- · Traditional drug discovery pipelines without AI explainability focus
Increased focus on multimodal data integration and more comprehensive biological modeling in AI-driven drug discovery.
Development of new AI models and explainability frameworks to address limitations highlighted by this research, potentially leading to more robust and safer drug candidates.
Regulatory bodies may begin to demand higher standards of explainability and broader data considerations for AI models used in drug development, impacting market entry for new drugs.
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Read at arXiv cs.LG