We proceeded to employ ENets to predict compact EPO-SCNPs with Rg < 7 nm and Porod exponent from 3.7-4.0 and randomly sampled 30 EPO-SCNPs which were synthesized and characterized to validate the model. While the initial random screen only resulted in 5% of SCNPs with Porod exponent ≥ 3.5, about 58% of the ENets predicted SCNPs exhibited a Porod exponent ≥ 3.5. Not only did this ML-guided approach improve SCNP design efficiency, SHAP analysis uncovered interesting design features such as the importance of DMA and BMA monomer content on Porod exponent and Rg. In conclusion, our work has illustrated that data-driven design provides a much-needed solution to the often-unintuitive problem of nanoparticle design and provides an potential framework for understanding synthetic macromolecule design-structure relationships.