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Artificial Intelligence and Predictive Models

(112) TuNa-AI: A Hybrid Kernel Machine to Design Tunable Nanoparticles for Drug Delivery

Introduction:


Artificial intelligence stands to accelerate the development of nanoparticles for drug delivery, but current methodologies either focus on the identification of materials or adjusting of relative ratios of multi-component systems. Here, we developed a bespoke hybrid kernel machine integrating molecular learning and relative composition inference to engineer drug-excipient nanoparticles [1] with new components and tunable composition [2]. Our approach identified nanoformulations that encapsulate previously inaccessible drugs and can also guide excipient reduction.




Learning Objectives:

  • utilize our proposed platform for nanoparticle identification and formulation optimization.
  • appreciate the feasibility of combining automation and machine learning for formulation design.
  • adapt the concept of hybrid kernel design for specific machine learning tasks.