Designing nanomedicines for targeted drug delivery often requires complex preparation steps involving both supramolecular self-assembly and multiple chemical modifications. These processes are generally difficult to predict, execute, and control. The relatively new field of  nanoinformatics uses machine learning and data mining to facilitate preparation processes and drug selection. I will present methods to predict successful formulations of hydrophobic kinase inhibitor molecules based on their molecular structures. In addition, I will propose additional methods to optimize formulations for drugs that are not predicted to form stable nanoparticles.

Nanoinformatics in drug delivery: Matching drugs to carriers

2019 Fall National Meeting

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