Exploration of the nanomedicine-design space with high-throughput screening and machine learning

Gokay Yamankurt, Eric J. Berns, Albert Xue, Andrew Lee, Neda Bagheri, Milan Mrksich, Chad A. Mirkin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

109 Scopus citations


Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining structure–activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. First, we identified ~1,000 candidate SNAs on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. Second, we developed a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and used a mass spectrometry assay to rapidly measure SNA immune activation. Third, we used machine learning to quantitatively model SNA immune activation and identify the minimum number of SNAs needed to capture optimum structure–activity relationships for a given SNA library. Our methodology is general, can reduce the number of nanoparticles that need to be tested by an order of magnitude, and could serve as a screening tool for the development of nanoparticle therapeutics.

Original languageEnglish (US)
Pages (from-to)318-327
Number of pages10
JournalNature Biomedical Engineering
Issue number4
StatePublished - Apr 1 2019

ASJC Scopus subject areas

  • Bioengineering
  • Biotechnology
  • Biomedical Engineering
  • Medicine (miscellaneous)
  • Computer Science Applications


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