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<p>Breast cancer’s genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug–gene–disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.</p>

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AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Rahaf M. Ahmad,
  • Salahdein Aburuz,
  • Bassam R. Ali,
  • Noura AlDhaheri

摘要

Breast cancer’s genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug–gene–disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.