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

Abstract

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 19F 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 <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

Machine learning strategies for the structure-property relationship of copolymers

“… (B) Compositions of eight copolymer samples and their SNR values. The figure is reprinted with permission from ref ( Reis et al., 2021 ). Copyright 2021 American Chemical Society.…”

“… The figure is reprinted with permission from ref ( Reis et al., 2021 ). Copyright 2021 American Chemical Society.…”

“…Dataset two is from an experimental study of the high-contrast 19 F magnetic resonance imaging (MRI) agents. The measured 19 F nuclear magnetic resonance (NMR) signal-to-noise ratio (SNR) indicates the performance of copolymers as 19 F NMR agents ( Reis et al., 2021 ). Dataset three is for specific polyhydroxyalkanoate (PHA)-based polymers and their glass transition temperature ( Pilania et al., 2019 ).…”

“…They assumed all copolymers to be random copolymers; thus, only monomer composition information is included in ML models without considering their sequences. Based on random copolymers and homopolymers, Hanaoka (2020) , Leibfarth et al., ( Reis et al., 2021 ), Kosuri et al. (2022) , Shi et al.…”

Feasibility and Application of Machine Learning Enabled Fast Screening of Poly-Beta- Amino-Esters for Cartilage Therapies

“…However, the actual costs are generally a commercial con dential information and, therefore, such estimates may not fully capture the complete investments required 4 . The try-and-error approach to molecule development, particularly during the initial design and make phases of the design-make-test-analyse (DMTA) discovery cycle, is often is directed by human intuition, which is inherently biased and limited in knowledge, thus slowing drug development 5 . In such contest, the ability of data-driven in-silico prediction tools to model outcomes without the need to physically prepare candidates and run experiments would enable a fast throughput screening of candidate molecules and thus reducing both the time and monetary investments required to identify lead candidates [6][7][8][9] .…”