We hypothesized that an ML-guided approach can be leveraged in an SCNP synthetic workflow to identify difficult-to-design compact and rigid nanoparticles. First, we synthesized over 1000 amphiphilic random heteropolymer backbones containing varied composition (neutral, cationic, and hydrophobic monomers), degree of polymerization (DP) (200, 300, and 400), functionalization (2 kDa PEG, TRAIL mimetic peptide, or EPO mimetic peptide), and valency (2, 5, and 10 mol%). Synthesized copolymers were characterized by SAXS and DLS to provide compactness and flexibility information that was then compared against a parallel dataset of over 300 protein structures extracted from the PDB with analogous theoretical compactness and flexibility. The dataset of over 1000 synthesized copolymers were leveraged to train evidential neural networks (ENets) to provide novel predictions for compact SCNPs. This approach successfully identified novel SCNP structures that demonstrated remarkable compactness. Further, we performed post hoc analysis of our data to understand how copolymer features influence SCNP compactness and flexibility (Figure ). Figure . Schematic of ML-guided approach for designing SCNPs. Proteins undergo a complex folding process and possess a high level of structural order. Using synthetic materials, a ML approach is utilized to model important design (DP, polymer composition, valency, and concentration) and structural features (Rh, Rg, compactness as Rg/Rh, and Porod exponent) to provide predictions for synthesizing novel copolymers with desired properties.