IPECnet: ML model for predicting the area of water solubility of interpolyelectrolyte complexes

Interpolyelectrolyte complexes (IPECs) are known for years as classic representatives of smart polymers. The solubility of IPECs in water-salt media is driven by numerous factors connected with polymer component parameters and media composition. This work is devoted to the development of the world's first machine learning-based model for predicting the area of existence of water-soluble IPECs for solving biomedical problems. A new approach is proposed that takes into account both the physico-chemical properties of polyelectrolytes and the chemical structures of their monomeric units. The developed approach is universal and can be used to predict the properties of multicomponent systems of a different chemical nature. The results of the work were applied to select the composition of water-soluble IPECs for treatment of surfaces in order to create bactericidal coatings. The dataset and model structures are publicly available on GitHub.