AI-Guided Design of Nanomedicines for Personalized Cancer Therapy

Taliikwa Nicholas Ceaser

Department of Pharmacognosy Kampala International University Uganda

Email:ceaser.taliikwa@studwc.kiu.ac.ug

                                                                       ABSTRACT
Personalized cancer therapy aims to tailor treatment strategies to the unique molecular, genetic, and phenotypic characteristics of individual patients. Nanomedicine has emerged as a powerful enabler of this paradigm by improving drug delivery, enhancing tumor selectivity, and reducing systemic toxicity. However, the rational design of effective nanomedicines is challenged by the complexity of cancer biology, interpatient heterogeneity, and the vast design space of nanomaterial properties. Artificial intelligence (AI) offers transformative tools to address these challenges by integrating large-scale biological, chemical, and clinical datasets to guide nanomedicine design and optimization. AI-driven approaches, including machine learning, deep learning, and reinforcement learning, can predict nanoparticle behavior, therapeutic efficacy, and patient-specific responses with unprecedented accuracy. This review examines the role of AI in guiding the design of nanomedicines for personalized cancer therapy. We discuss how AI enables data-driven nanoparticle engineering, patient stratification, and adaptive treatment strategies. Current applications, emerging platforms, and translational challenges are critically evaluated. By bridging nanotechnology and precision oncology, AI-guided nanomedicine holds significant promise for realizing truly personalized cancer treatment.

Keywords: artificial intelligence; nanomedicine; personalized cancer therapy; machine learning; precision oncology.

CITE AS: Taliikwa Nicholas Ceaser (2026). AI-Guided Design of Nanomedicines for Personalized Cancer Therapy. NEWPORT INTERNATIONAL JOURNAL OF BIOLOGICAL AND APPLIED SCIENCES 7(2):73-78. https://doi.org/10.59298/NIJBAS/2026/7.2.7378