Artificial neural network modeling on the polymer-electrolyte aqueous two-phase systems involving biomolecules

In this work, modelling studies on the binodal curve behavior of polymer-electrolyte ATPS and the partitioning of biomolecules in these aqueous electrolyte solutions are carried out. First, a comprehensive database targeting the studied systems is established. Then, a novel modeling strategy that combines a well-known machine learning algorithm, i.e., artificial neural network (ANN) and group contribution (GC) method is proposed. Based on this modeling strategy, an ANN-GC model (ANN-GC model1) is built to describe the binodal curve behavior of polymer-electrolyte ATPS, while another ANN-GC model (ANN-GC model2) is developed to predict the partition of biomolecules in these biphasic systems. Furthermore, the obtained results also indicate that the tie-line length of polymer-electrolyte ATPS calculated from ANN-GC model1 can be directly used in ANN-GC model2 for predicting the partition performance coefficient of biomolecules in these ATPS.