A class of synthetic macromolecules that mimic proteins are referred to as single-chain polymer nanoparticles (SCNPs) which can collapse into higher ordered structures. These nanoparticles have potential as enzyme or protein mimetics, sensing nanomaterials, and drug delivery systems. Of particular interest is the use of SCNPs as multivalent therapeutics because of their biophysical tunability and therefore ligand presentation. Multivalent strategies provide avidity or accumulated binding strength from presentation of multiple ligands and can be explained mathematically using statistical thermodynamics. Greater valency theoretically increases permutation entropy, or number of possible ligand-receptor interaction pairs, and thus results in a more negative or favorable binding free energy. This effect tends to be more pronounced for weaker binding ligands (e.g. peptides) which have a faster off-rate and allow for more potential interactions. Providing some optimal amount of polymer scaffold flexibility can impart sufficient conformational freedom for ligands to position towards receptors and be in position to interact and bind. Recently, Chapman and co-workers utilized a similar strategy with end-functionalized star polymer-peptide conjugates to mimic the chemotherapeutic protein TNF-related apoptosisinducing ligand (TRAIL). Multivalency has also been implemented to improve binding affinity and biological activity of erythropoietin (EPO) mimetic peptides, illustrating the appeal of this approach. To effectively mimic protein therapeutics, there is a requirement to further optimize SCNP design features that affect downstream signaling such as compactness, flexibility, and valency. While complexity in the form of orthogonal chemistry may be required, a polymer coil-toglobule transition is largely driven by hydrophobic collapse to form a core that is shielded from aqueous solvent. Hydrogen bonding, π-π stacking, and electrostatics can both stabilize SCNPs and enhance solubility. There is an obvious relationship between polymer chain flexibility and compactness: flexible polymers undergo intra-chain cross-linking and collapse and interacting segments tend to increase in stiffness with reduced conformational degrees of freedom. Despite our fundamental understanding of polymer physics, there is no established protocol to design polymers with a desired conformation because of the inability to experimentally probe the entire chemical design space. Traditional polymer chemistry techniques such as reversible addition-fragmentation chain transfer (RAFT) polymerization and atom transfer radical polymerization (ATRP) must be conducted in inert conditions. Introduced within the last decade were air-tolerant photoinduced electron/energy transfer-RAFT (PET-RAFT), enzymeassisted ATRP, enzyme-assisted RAFT (Enz-RAFT), and ring-opening polymerization (ROP) through water which have enabled polymerizations to be carried out directly in microplate format. To further the ability to complete combinatorial chemistry, our group previously demonstrated the potential of a plate-based gel filtration purification, high-throughput structural characterization of SCNPs, and the adaptation of liquid handling robotics to complete polymer synthesis and post-polymerization functionalization. Others such as Gibson and co-workers have also utilized liquid handling robotics for preparing and testing polymer libraries. However, utilizing only high-throughput approaches to synthesize and characterize SCNPs runs the risk of inefficiency, false positives and negatives, and difficulty with interpreting large datasets. In previous work, we conducted high-throughput screening of a large combinatorial library (> 450 copolymers) via dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) to identify compact nanostructures. Due to polymer hydrophobicity and having a relatively small dataset, we were unable to define a model to quantitatively inform further design, thus serving as a motivation for this study. For such complex and non-intuitive material design problems, machine learning (ML) is emerging as an exciting approach for informing design.