Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
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DOI：
10.1021/jacs.1c08181
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发表时间：
2021-10-12
影响因子：
15
通讯作者：
Leibfarth, Frank A.
中科院分区：
文献类型：
--
作者：
Reis, Marcus;Gusev, Filipp;Leibfarth, Frank A.
现代聚合物科学受到多维性的困扰。通过将单体的组合包括到统计共聚物中而施加的大的化学空间阻碍了聚合物合成和表征技术，并且限制了系统地研究结构-性质关系的能力。为了在F-19磁共振成像（MRI）试剂的背景下应对这一挑战，我们采用了一种计算机引导的材料发现方法，该方法结合了自动流合成和机器学习（ML）方法开发的协同创新。开发了一种软件控制的连续聚合物合成平台，以实现迭代的实验-计算循环，从而在六变量组成空间内合成了397种独特的共聚物组合物。ML确定了非直观的设计标准，通过探索10种性能优于最先进材料的共聚物组合物来实现。
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of F-19 magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring 10 copolymer compositions that outperformed state-of-the-art materials.