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Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation. | LitMetric

AI Article Synopsis

  • * This approach resulted in conducting around 900 reactions in just 192 hours while drastically reducing reagent use to about 10% of that required in fully continuous methods.
  • * By integrating experimental data with a machine learning model, the research demonstrates improved predictions and analysis for optimizing drug discovery processes, ultimately enhancing the design, make, test, and analysis cycle.

Article Abstract

For the discovery of new candidate molecules in the pharmaceutical industry, library synthesis is a critical step, in which library size, diversity, and time to synthesise are fundamental. In this work we propose stopped-flow synthesis as an intermediate alternative to traditional batch and flow chemistry approaches, suited for small molecule pharmaceutical discovery. This method exploits the advantages of both techniques enabling automated experimentation with access to high pressures and temperatures; flexibility of reaction times, with minimal use of reagents (μmol scale per reaction). In this study, we integrate a stopped-flow reactor into a high-throughput continuous platform designed for the synthesis of combinatory libraries with at-line reaction analysis. This approach allowed ∼900 reactions to be conducted in an accelerated timeframe (192 hours). The stopped flow approach used ∼10% of the reactants and solvents compared to a fully continuous approach. This methodology demonstrates a significantly improved synthesis success rate of smaller libraries by simplifying the implementation of cross-reaction optimisation strategies. The experimental datasets were used to train a feed-forward neural network (FFNN) model providing a framework to guide further experiments, which showed good model predictability and success when tested against an external set with fewer experiments. As a result, this work demonstrates that combining experimental automation with machine learning strategies can deliver optimised analyses and enhanced predictions, enabling more efficient drug discovery investigations across the design, make, test and analysis (DMTA) cycle.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601405PMC
http://dx.doi.org/10.1039/d2sc03016kDOI Listing

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