Peptides that comprise the functional subunits of proteins have been conjugated to versatile materials (biomolecules, polymers, surfaces and nanoparticles) in an effort to modulate cell responses, specific binding affinity and/or self-assembly behavior. However, the efficient and convenient synthesis of peptide-conjugates, especially the constructs with multiple types of peptide functionality remains challenging. In this critical review, we focus on “click” reactions that have been used to synthesis peptide-functionalized conjugates, introducing their reaction conditions, specifically elucidating parameters that influence reaction kinetics and total conversion, and highlighting examples that have been completed recently. Moreover, orthogonal “click” reactions that synthesize multi-functional biomaterials in a one-pot or sequential manner are noted. Through this review, a comprehensive understanding of “click” reactions aims to provide insight on how one might choose suitable “click” reactions to constitute peptide-functionalized molecules/surfaces/matrices for the development of advanced biomaterials.

This review highlights the research on degradable polymeric tissue adhesives for surgery and tissue engineering. Included are a comprehensive listing of specific uses, advantages, and disadvantages of different adhesive groups. A critical evaluation of challenges affecting the development of next generation materials is also discussed, and insights into the outlook of the field are explored.

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.