Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis

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 rel...

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Bibliographic Details
Published in:Journal of the American Chemical Society 2021-10, Vol.143 (42), p.17677-17689
Main Authors: Reis, Marcus, Gusev, Filipp, Taylor, Nicholas G, Chung, Sang Hun, Verber, Matthew D, Lee, Yueh Z, Isayev, Olexandr, Leibfarth, Frank A
Format: Article
Language:English
Subjects:
Contrast Media - chemical synthesis
Contrast Media - chemistry
Fluorine - chemistry
Machine Learning
Magnetic Resonance Imaging
Polymers - chemical synthesis
Polymers - chemistry
Software
Solubility
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Summary: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 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.
ISSN:0002-7863
1520-5126
DOI:10.1021/jacs.1c08181