Prediction of biological response for large combinatorial libraries of biodegradable polymers: Polymethacrylates as a test case

A large virtual combinatorial library of polymethacrylates was, for the first time, designed for computer-aided prediction of biorelevant and material properties and focused polymer synthesis. The distinguishing features of this virtual library include its size (about 40 000 compounds), its explicit representation of relatively long polymer chains, and its accounting for different compositions in the case of copolymers and terpolymers. A subset of 79 polymers taken from a representative sub-library of 2000 polymethacrylates was employed to build initial QSPR-based polynomial neural network models, which were then deployed to predict cell attachment, cell growth, and fibrinogen adsorption on polymer surfaces for these 2000 polymethacrylates. The agreement between predicted and experimentally measured property values for the 50 polymethacrylate copolymers within this virtual polymer space encourages further pursuit of polymethacrylate-based biomaterials, and justifies more extensive deployment of computational models derived from larger experimental data sets for the rational design of biorelevant polymers endowed with targeted performance properties.