Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials
Samantha Stuart, Jeffrey Watchorn, Frank Gu
Abstract
Abstract It has proved challenging to represent the behavior of polymeric macromolecules as machine learning features for biomaterial interaction prediction. There are several approaches to this representation, yet no consensus for a universal representational framework, in part due to the sensitivity of biomacromolecular interactions to polymer properties. To help navigate the process of feature engineering, we provide an overview of popular classes of data representations for polymeric biomaterial machine learning while discussing their merits and limitations. Generally, increasing the accessibility of polymeric biomaterial feature engineering knowledge will contribute to the goal of accelerating clinical translation from biomaterials discovery.