Mapping Biomaterial Complexity by Machine Learning

In short

Biomaterials rarely fail or succeed for one obvious reason. Performance usually comes from a combination of small structural details interacting at once. Testing those combinations one experiment at a time does not scale. This review sets out how high-throughput experimentation paired with machine learning changes the search: run many conditions in parallel, keep every data point including the failures, and train models that map structure to function across the whole space rather than at a few sampled points.

Why it matters

• Covers five application areas in one place (tissue engineering, gene delivery, drug delivery, protein stabilization and antifouling materials) rather than a single material class.
• Treats data mining as a first-class method alongside experiment, showing where published data can substitute for benchwork.
• Written for experimentalists adopting ML, not for ML specialists: the framing is which model to reach for and what data it needs.

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.

Published abstract, reproduced from the version of record.

Keywords

• biomaterials
• high-throughput experimentation
• machine learning
• structure-property relationships
• tissue engineering
• data mining

Cite this paper

Ahmed, E., Mulay, P., Ramirez, C., Tirado-Mansilla, G., Cheong, E. & Gormley, A. J. (2024). Mapping Biomaterial Complexity by Machine Learning. Tissue Engineering Part A, 30(19-20), 662-680. https://doi.org/10.1089/ten.tea.2024.0067

@article{ahmed2024mapping,
  title   = {Mapping Biomaterial Complexity by Machine Learning},
  author  = {Ahmed, Eman and Mulay, Prajakatta and Ramirez, Cesar and Tirado-Mansilla, Gabriela and Cheong, Eugene and Gormley, Adam J.},
  journal = {Tissue Engineering Part A},
  volume  = {30},
  number  = {19-20},
  pages   = {662-680},
  year    = {2024},
  doi     = {10.1089/ten.tea.2024.0067}
}