High-Throughput Polymer Design for the Biological Interface

Abstract High-throughput experimentation is transforming synthetic polymer design for biological applications, enabling data-driven approaches that map the vast chemical design space accessible to synthetic macromolecules. In this Viewpoint, we survey recent advances in high-throughput methodologies for discovering polymers that interface with biological systems. We first review synthetic strategies, such as well-plate polymerizations, immobilized and solid-phase synthesis, and continuous flow chemistry, that enable parallelized, miniaturized access to large polymer libraries. We then discuss screening methods for structural characterization, target-binding interactions, and functional performance in cellular and biological contexts. We highlight how these readouts capture the disordered, dynamic nature of synthetic polymers relative to structured biomacromolecules. Finally, we examine how machine learning, active learning, self-driving laboratories, and emerging agentic AI tools are being integrated with high-throughput workflows to accelerate inverse design and autonomous experimentation. We underscore the potential of increasingly automated discovery in future years and the need for rigorous, application-appropriate workflow design. Together, these synthesis, characterization, and computational strategies form a synergistic pipeline poised to accelerate the discovery of next-generation polymer therapeutics and biomaterials.