Zhou Bixia, Li Xin, Pan Yuchen, He Bingfang, Gao Bingbing
College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China.
School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, China.
Colloids Surf B Biointerfaces. 2025 Nov;255:114970. doi: 10.1016/j.colsurfb.2025.114970. Epub 2025 Jul 19.
The Fourth Industrial Revolution (Industry 4.0) has marked a shift from traditional materials to the era of smart materials. The integration of artificial intelligence (AI) with biomaterials is transforming the biosensing and biomedical fields. Although AI-assisted biomaterial manufacturing holds significant promise, the design and synthesis of smart materials remain in the early stages. To accelerate the implementation of AI-assisted biomaterials in fields such as biomedicine and biological intelligent systems, various algorithms have been developed to predict material properties, enable material de novo design, and establish a foundation for the development of next-generation multifunctional biomaterials. This review presents a comprehensive overview of AI-assisted biomaterial design, property prediction, fabrication, and potential biomedical applications. Recent advances in AI-driven protein engineering relevant to materials science are summarized, followed by an analysis of AI's role in designing, predicting, and optimizing next-generation biomaterials. The influence of AI-assisted systems on the structural and functional properties of biosmart materials is also explored. Applications such as therapeutic diagnostics, electronic skin (e-skin), biosensing, and other biomedical technologies are highlighted. Finally, current challenges and future perspectives are discussed, with emphasis on the transformative potential of AI in advancing materials science and biomedicine, as well as its ability to address previously intractable problems.
第四次工业革命(工业4.0)标志着从传统材料向智能材料时代的转变。人工智能(AI)与生物材料的融合正在改变生物传感和生物医学领域。尽管人工智能辅助生物材料制造具有巨大潜力,但智能材料的设计和合成仍处于早期阶段。为了加速人工智能辅助生物材料在生物医学和生物智能系统等领域的应用,人们开发了各种算法来预测材料性能、实现材料的从头设计,并为下一代多功能生物材料的开发奠定基础。本文综述全面概述了人工智能辅助生物材料的设计、性能预测、制造以及潜在的生物医学应用。总结了与材料科学相关的人工智能驱动蛋白质工程的最新进展,随后分析了人工智能在设计、预测和优化下一代生物材料中的作用。还探讨了人工智能辅助系统对生物智能材料结构和功能特性的影响。重点介绍了治疗诊断、电子皮肤(e-skin)、生物传感和其他生物医学技术等应用。最后,讨论了当前面临的挑战和未来展望,强调了人工智能在推动材料科学和生物医学发展方面的变革潜力,以及其解决以往棘手问题的能力。