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.
In silico identification of potent protein
inhibitors
commonly requires prediction of a ligand binding free energy (BFE).
Thermodynamics integration (TI) based on molecular dynamics (MD) simulations
is a BFE calculation method capable of acquiring accurate BFE, but
it is computationally expensive and time-consuming. In this work,
we have developed an efficient automated workflow for identifying
compounds with the lowest BFE among thousands of congeneric ligands,
which requires only hundreds of TI calculations. Automated machine
learning (AutoML) orchestrated by active learning (AL) in an AL–AutoML
workflow allows unbiased and efficient search for a small set of best-performing
molecules. We have applied this workflow to select inhibitors of the
SARS-CoV-2 papain-like protease and were able to find 133 compounds
with improved binding affinity, including 16 compounds with better
than 100-fold binding affinity improvement. We obtained a hit rate
that outperforms that expected of traditional expert medicinal chemist-guided
campaigns. Thus, we demonstrate that the combination of AL and AutoML
with free energy simulations provides at least 20× speedup relative
to the naïve brute force approaches.
Modern polymer science is plagued by 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 19 F 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 non-intuitive design criteria identified by ML, which was accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19 F MRI agents and lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.
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