Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 397-399 |
| Number of pages | 3 |
| Journal | TRENDS IN CANCER |
| Volume | 4 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2018 |
Keywords
- caveolin-mediated endocytosis
- machine learning
- nanoinformatics
- nanomedicine
- tyrosine kinase inhibitors
All Science Journal Classification (ASJC) codes
- Oncology
- Cancer Research