Mapping Biomaterial Complexity by Machine Learning

Abstract

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure–function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure–function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

Impact Statement

Material scientists have recently begun to leverage machine learning to transform biomaterial discovery. These data-driven tools enable accurate and rapid predictions of biomaterial properties by mapping complex structure–function relationships. From optimizing drug encapsulation to designing functional scaffold architectures, machine learning helps researchers make informed decisions for accelerating innovation while ensuring high efficacy.