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<p>Cancer remains a significant global health challenge mainly because of&#xa0;tumor heterogeneity, systemic toxicity&#xa0;and the&#xa0;drug resistance caused by conventional treatments. These factors complicate effective therapy and patient outcomes. This review critically analyzes how AI, multi-omics, and nanomedicine have enhanced the field of precision oncology, particularly in the design of nanocarriers with AI assistance, patient stratification through biomarkers, and predictive modeling of therapeutic responses. The role of bioinformatics resources like TCGA and COSMIC is discussed in the context of genomic and molecular profiling for personalized treatment strategies. Furthermore, computational methods such as molecular docking, receptor–ligand modeling, stochastic simulations, and ADMET prediction are assessed for their role in optimizing nanocarriers and enhancing drug delivery capabilities. The review also underscores the benefits of integrating multi-omics in the recognition of clinically relevant biomarkers, which will facilitate the greater accuracy of AI-based predictive models in cancer nanotherapy. Finally, important barriers for translation are discussed, such as data heterogeneity, the interpretability of models, scalability of nanocarriers' production, and clinical validation, to highlight the future pathway toward the development of clinically feasible AI-driven personalized nanomedicine.</p>

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AI Driven Computational Nanomedicine for Personalized Cancer Therapy

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Komal Farghama,
  • Muhammad Saleem Khan,
  • Muhammad Wajid,
  • Shifa Shaffique

摘要

Cancer remains a significant global health challenge mainly because of tumor heterogeneity, systemic toxicity and the drug resistance caused by conventional treatments. These factors complicate effective therapy and patient outcomes. This review critically analyzes how AI, multi-omics, and nanomedicine have enhanced the field of precision oncology, particularly in the design of nanocarriers with AI assistance, patient stratification through biomarkers, and predictive modeling of therapeutic responses. The role of bioinformatics resources like TCGA and COSMIC is discussed in the context of genomic and molecular profiling for personalized treatment strategies. Furthermore, computational methods such as molecular docking, receptor–ligand modeling, stochastic simulations, and ADMET prediction are assessed for their role in optimizing nanocarriers and enhancing drug delivery capabilities. The review also underscores the benefits of integrating multi-omics in the recognition of clinically relevant biomarkers, which will facilitate the greater accuracy of AI-based predictive models in cancer nanotherapy. Finally, important barriers for translation are discussed, such as data heterogeneity, the interpretability of models, scalability of nanocarriers' production, and clinical validation, to highlight the future pathway toward the development of clinically feasible AI-driven personalized nanomedicine.