Applied machine learning as a driver for polymeric biomaterials design

Abstract

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.

Partnerships and collaboration drive innovative graduate training in materials informatics

“…Projects are intended to become a chapter in each team member’s dissertation and disseminated through conference presentations and manuscript publications. Among the 14 program projects to date: A team of chemistry and biostatistics doctoral students analyzed databases to improve biomedical polymers discovery using ML ( 7 ). Another team comprised of civil engineering and materials science doctoral students trained an ML model to predict the mechanical profile of porous materials ( 8 ).…”

Trends in bioactivity: inducing and detecting mineralization of regenerative polymeric scaffolds

“…For HTS and ML to be effective toward generating and predicting attributes of bioactive scaffolds, there must be standardized protocols for testing, which is currently lacking. 122 In addition to selecting optimal material combinations, AI can be helpful in determining suitable 3D printing fabrication techniques and scaffold structure depending on the patient and location of the bone defect. Integrating AI with computer-aided design (CAD) to 3D print patient-specific scaffolds, especially with complex geometries, can reduce the time to generate scaffold structures and facilitate the selection of scaffold parameters ( e.g.…”

The state-of-art polyurethane nanoparticles for drug delivery applications

“…This accelerates safety assessments and ensures the reliability of drug delivery systems. By amalgamating data from various sources such as molecular databases, clinical trials, and patient records, AI systems offer comprehensive decision-making support to researchers and healthcare professionals engaged in polyurethane drug delivery ( McDonald et al, 2023 ).…”