While AlphaFold supplies protein structural information from specific amino acid sequences, a design-structure gap exists for synthetic macromolecules. As high-throughput polymer chemistry provides new opportunity for data-driven design, we aimed to bridge the gap between synthetic SCNP design features and structural characteristics (compactness and flexibility) where sequencelevel control is not yet possible. It is clear from this large dataset of copolymers and copolymer conjugates that the synthesis and design of SCNPs is a multi-objective problem with high complexity as there are design features and measured characteristics that are interrelated. SCNPs contain nearly an infinite chemical space with the potential to select features such as DP, monomer composition, architecture, and valency, but the question remains of how to select an initial polymer library. In this work, we intuitively selected copolymers based on theoretical log P and attempted to prescreen these combinations using DLS. Employing our own logic can result in inefficiencies and may require a more iterative process which can be difficult to implement experimentally especially with a technique such as SAXS. Another challenge with complex copolymer conjugates involves deconvoluting physical contributions between polymer and peptide which would require other techniques such as NMR or small-angle neutron scattering (SANS). In addition to all of these factors, it is likely difficult to synthesize structures with the same level of order and folding as proteins mainly because of the lack of sequence-level control. Until advances are made to achieve this level of control with synthetic ease, polymer chemists may need to rely on quantifying reactivity ratios, illustrating compositional drift, and potentially designing for molecular weight distribution. Others are even making efforts to increase complexity with polymer design, biologically inspired monomers, and multi-orthogonal approaches. Combining informative approaches such as ML, simulations, and structural characterization can only strengthen the SCNP design paradigm.