TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery

Artificial intelligence (AI) has the potential to transform nanoparticle development for drug delivery; however, existing strategies typically optimize either material selection or component ratios in isolation. To enable simultaneous optimization of both, we integrated an automated liquid handling platform with machine learning to systematically explore the nanoparticle formulation space. A data set comprising 1275 distinct formulations (spanning drug molecules, excipients, and synthesis molar ratios) was generated, resulting in a 42.9% increase in successful nanoparticle formation through composition optimization. We developed a bespoke hybrid kernel machine that couples molecular feature learning with relative compositional inference, enhancing the modeling of formulation outcomes across chemical spaces. This hybrid kernel significantly improved prediction performance across three kernel-based algorithms, with a support vector machine (SVM) achieving superior performance when using our kernel compared to standard kernels and outperforming all other machine learning architectures, including transformer-based deep neural networks. Using SVM-guided predictions, we successfully formulated the difficult-to-encapsulate venetoclax with optimized taurocholic acid ratios, yielding enhanced in vitro efficacy against Kasumi-1 leukemia cells. In a second case study, our AI-guided platform reduced excipient usage by 75% in a trametinib formulation while preserving the in vitro efficacy and in vivo pharmacokinetics relative to the standard formulation. Taken together, this study establishes a generalizable framework that combines robotic experimentation, kernel machine learning, and experimental validation to accelerate nanoparticle composition optimization for drug delivery.