Zoom Link: https://weizmann.zoom.us/j/92447973616?pwd=UWJkRWdraGFVQjdPb3ByWis1bDk2Zz09
Development of targeted nanoparticle for personalized cancer therapeutics often requires complex synthetic schemes involving both supramolecular self-assembly and multiple chemical modifications. These processes are generally difficult to predict, execute, and control. I will describe a new method to accurately and quantitatively predict self-assembly of kinase inhibitors drug molecules into nanoparticles based on their molecular structures. The drugs assemble with the aid of new kind of excipient comprised of highly conjugated sulfated molecule into particles with ultra-high drug loadings of up to 90%. Using quantitative structure-nanoparticle assembly prediction (QSNAP) calculations and machine learning, a new algorithm was developed as highly predictive indicators of both nano-self assembly and nanoparticle size with unprecedented accuracy.
