Perspectives on development of biomedical polymer materials in artificial intelligence age

Area of Science:

• Biomedical Engineering
• Materials Science
• Polymer Chemistry

Background:

• Traditional polymer preparation relies on experience, facing challenges in vast design spaces for biomedical applications.
• The development of new biomedical polymers is crucial but hindered by complex material design processes.

Purpose of the Study:

• To review the application of machine learning (ML) in accelerating the design of biomedical polymers.
• To analyze the progress and opportunities of ML in biopolymer synthesis and property prediction.

Main Methods:

• Summarization of material databases and open-source determination tools for biopolymer research.
• Review of molecular generation methods and ML models applied to biopolymer synthesis and property prediction.

Main Results:

• Machine learning offers a powerful approach to overcome challenges in biomedical polymer design.
• Successful cases and latest progress in ML for biomedical polymers are presented.

Conclusions:

• Machine learning is a key enabler for efficient and customized design of biomedical polymers.
• ML is expected to drive innovation and meet the growing demand for tailored biomaterials.