Artificial intelligence-assisted design, synthesis and analysis of smart biomaterials

Abstract

Smart biomaterials that can self-adapt or respond to microenvironmental factors or external signals hold excellent potential for a variety of biomedical applications, from biosensing, drug delivery, and cell therapy to tissue engineering. The complexity of smart biomaterials, including the rational design of their structure and composition, the accurate analysis and prediction of their properties, and the automatic and scale-up synthesis remains a critical challenge but can be addressed by the recent rise of artificial intelligence (AI). To bridge the literature gap, the current mini-review will introduce the background of why marrying AI with smart biomaterials is essential and how biomaterial scientists can integrate machine learning (ML) and AI for the discovery, design, analysis, and synthesis of smart biomaterials. For this purpose, the basic principles of ML and AI will first be introduced so that biomaterial scientists can use ML and AI as a tool for basic research. Next, representative examples of using AI to high throughput screen and establish big data of structure-function relationship of smart biomaterials responding to both chemical, biological, and physical signals. Most importantly, the applications of the AI-designed or AI-discovered biomaterials will be overviewed, with a focus on the field of tissue engineering. Lastly, new directions, such as robot-chemists-assisted fabrication of biomaterials will be highlighted. Taken together, by engaging biomaterial scientists with the most recent updates in AI material science, we expect to observe continuous growth of the field of AI for science and benefit clinical translation of smart biomaterials for treating a variety of diseases.

Keywords

artificial intelligence / materiomics / smart biomaterials / stem cell / tissue engineering

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