Designing personalized cancer nanomedicines is a challenging process. The emerging field of nanoinformatics can facilitate this process by enabling computational design of nanocarrier-encapsulated drugs. Recent data show that quantitative structure-nanoparticle assembly calculations predict particle formation and size, and can lead to safer and more effective personalized cancer therapeutics.
Keywords: caveolin-mediated endocytosis; machine learning; nanoinformatics; nanomedicine; tyrosine kinase inhibitors.
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