Evaluation of machine learning algorithms to predict the hydrodynamic radii and transition temperatures of chemo-biologically synthesized copolymers.

Elastin-like polypeptides (ELP) belong to a family of recombinant polymers that shows great promise as biocompatible drug delivery and tissue engineering materials. ELPs aggregate above a characteristic transition temperature (Tt). We have previously shown that the Tt and size of the resulting aggregates can be controlled by changing the ELP's solution environment (polymer concentration, salt concentration, and pH). When coupled to a synthetic polyelectrolyte, polyethyleneimine (PEI), ELP retains its Tt behavior and gains the ability to be crosslinked into defined particle sizes. This paper explores several machine learning models to predict the Tt and hydrodynamic radius (Rh) of ELP and two ELP-PEI polymers in varying solution conditions. An exhaustive design of experiments matrix consisting of 81 conditions of interest with varying salt concentration (0, 0.2, 1 M NaCl), pH (3, 7, 10), polymer concentration (0.1, 0.17, 0.3 mg/mL), and polymer type (ELP, ELP-PEI800, ELP-PEI10K) was investigated. The five models used in this study were multiple linear regression, elastic-net, support vector regression, multi-layer perceptron, and random forest. A multi-layer perceptron model was found to have the highest accuracy, with an R2 score of 0.97 for both Rh and Tt. This was followed closely by the random forest model, with an R2 of 0.94 for Rh and 0.95 for Tt. Feature importance was determined using the random forest and linear regression models. Both models showed that salt concentration and polymer type were the two most influential factors that determined Rh, while salt concentration was the dominant factor for Tt.