Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation. Issue 41 (7th October 2022)

• Record Type: Journal Article
• Authors: Avila, Claudio; Cassani, Carlo; Kogej, Thierry; Mazuela, Javier; Sarda, Sunil; Clayton, Adam D.; Kossenjans, Michael; Green, Clive P.; Bourne, Richard A.
• Is Part Of: Chemical science. Volume 13:Issue 41(2022)
• Page Start: 12087
• Page End: 12099
• Publication Date: 2022-10-07
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2sc03016k
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Combining experimental stopped flow automation with machine learning strategies can deliver optimised conditions and enhanced predictions, enabling more efficient design, make, test and analysis (DMTA) cycles. 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.