Data obtained from random screening SAXS and DLS experiments were utilized to train evidential regression networks (ENets); a type of deep neural network for prediction of Rg, Rh, Rg/Rh, and Porod Exponent. In this approach, a neural network is trained to predict the hyperparameters (𝛾, 𝓋, 𝛼, 𝛽) of a normal-inverse gamma (NIG) distribution representative of labeled data. This enables efficient prediction of complex data, while also learning uncertainty within data. To train the networks, relative incorporation of monomers, peptides, and degree of polymerization of SCNPs were represented as one-hot encoding vectors and normalized using Sci-Kit Learn's RobustScaler transform. ENets were trained to predict the normalized values of Rg, Rh, Rg/Rh, and Porod exponent after RobustScaler transformation. To determine effective ENet hyperparameters, we performed 10-Kfold cross-validation utilizing the Python KerasTuner package to minimize a combined loss function of negative log-likelihood (NLL) loss, a regularization coefficient, and mean squared error (MSE) as described elsewhere. Optimal hyperparameters that were identified on nine of ten folds were then utilized to make predictions on target Rg, Rh, Rg/Rh, and Porod Exponent values from the remaining fold. This process was repeated for each fold until predictions on all held-out folds were made. These 10 sets of held-out predictions were then utilized to validate model performance and determine a final set of hyperparameters for forward predictions of EPO-SCNPs (Figure ). To determine an appropriate value for our loss function regularization coefficient (L R ), values from 1e-5:1 were evaluated by observing model confidence on held-out data predictions. An L R of 0.1 was selected for further hyperparameter optimization as it demonstrated effective calibration of uncertainty on held out data (Figure ).