Department of Chemistry, Duke University, Durham, NC, USA.
Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.
Nat Commun. 2023 Aug 10;14(1):4838. doi: 10.1038/s41467-023-40459-8.
Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.
聚合物几乎存在于现代社会的各个方面，它们在医疗产品中的应用也同样广泛。尽管如此，在医学中使用的商业聚合物的多样性却低得惊人。多年来，人们投入了大量的时间和资源来开发新的聚合生物材料，以解决当前一代医用级聚合物未满足的需求。机器学习 (ML) 在该领域提供了一个前所未有的机会，可以避免试错合成的需要，从而减少为推进医疗治疗而进行新发现所需的时间和资源。目前，在聚合物设计中开创性地应用 ML 的工作已经采用组合和高通量实验设计来解决数据可用性问题。然而，缺乏与医学相关的参数（包括降解时间和生物相容性）的可用和标准化表征，这几乎是 ML 辅助生物材料设计的一个无法逾越的障碍。在此，我们在应用 ML 和生物医学聚合物设计的交叉点发现了一个空白，更广泛地强调了这一交汇点的当前工作，并展望了挑战和未来方向。
