Xue Kun, Wang FuKe, Suwardi Ady, Han Ming-Yong, Teo Peili, Wang Pei, Wang Shijie, Ye Enyi, Li Zibiao, Loh Xian Jun
Institute of Materials Research and Engineering, A∗STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore.
Mater Today Bio. 2021 Nov 23;12:100165. doi: 10.1016/j.mtbio.2021.100165. eCollection 2021 Sep.
Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.
生物材料学是一个跨学科研究领域,旨在从新材料(无论材料类型如何)中实现预期的生物学反应。该学科已经有许多令人兴奋的创新成果,但商业化过程却面临着从发现到产品流程漫长且沿途失败众多的问题。利用机器学习技术梳理大量数据可极大地加速成功进程。从非结构化的经验方法转向基于数据的开发策略有许多潜在益处。在此,我们讨论了近期关于机器学习在生物材料发现与设计中的应用的工作,包括新型聚合物、金属、陶瓷和纳米材料,以及机器学习如何与3D打印的新兴用例相结合。我们还讨论了更紧密整合机器学习以实现这一令人兴奋的可能性的步骤。