Artificial-intelligence-driven rational design of nanoparticle-mediated targeted protein degradation

Targeted protein degradation (TPD) has emerged as a powerful therapeutic strategy to eliminate disease-associated proteins that are inaccessible to conventional inhibition. Among TPD approaches, nanoparticle-mediated degradation, particularly through nanoparticle-mediated targeting chimerics (NPTACs) that induce receptor-mediated endocytosis and lysosomal degradation, offers unique advantages in modular design, target selectivity, and delivery potential. However, the complexity of these systems, determined by factors including particle size, surface charge, PEGylation, and ligand properties, poses significant challenges in predicting which formulations will effectively mediate intracellular trafficking and protein degradation. This perspective proposes artificial intelligence (AI) as a transformative tool to enable rational, predictive, and personalized NPTAC design. By integrating curated datasets, machine learning, generative modeling, and mechanistic insights, AI can navigate vast design spaces and accelerate translation. We outline key opportunities, challenges, and a future vision for intelligent, patient-tailored degrader nanomedicines.