Prediction of the Partition Coefficients of Biomolecules in Polymer–Polymer Aqueous Two-Phase Systems Using the Artificial Neural Network Model

Abstract

In this work, an artificial neural network model was used to obtain the partition coefficients of biomolecules in polymer–polymer aqueous two-phase systems. In the artificial neural network, the partition coefficient of a biomolecule depends on the difference between concentrations of poly (ethylene glycol), dextran in the top and bottom phases, temperature and molecular weights of poly (ethylene glycol), dextran, and the biomolecule. The network topology is optimized and the (6-1-1) architecture is found using optimization of an objective function with sequential quadratic programming (SQP) method for 450 experimental data points. The results obtained from the neural network of the partition coefficients of biomolecules in polymer–polymer aqueous two-phase systems were compared with those from the modified Flory–Huggins model. Comparisons showed that the artificial neural network is 50% more accurate than the Flory–Huggins model in obtaining partition coefficients of biomolecules in polymer–polymer aqueous two-phase systems.