Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation.
Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation.
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DOI：
10.1039/d2sc03016k
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发表时间：
2022-10-26
期刊：
影响因子：
8.4
通讯作者：
Bourne, Richard A.
中科院分区：
文献类型：
--
作者：
Avila, Claudio;Cassani, Carlo;Kogej, Thierry;Mazuela, Javier;Sarda, Sunil;Clayton, Adam D.;Kossenjans, Michael;Green, Clive P.;Bourne, Richard A.
为了在制药工业中发现新的候选分子，文库合成是关键步骤，其中文库大小、多样性和合成时间是基本的。在这项工作中，我们提出了停流合成作为一种中间替代传统的批量和流动化学方法，适合小分子药物的发现。该方法利用了两种技术的优点，能够在高压和高温下进行自动化实验;反应时间灵活，试剂使用量最少（每个反应的μmol规模）。在这项研究中，我们将停流反应器集成到一个高通量连续平台中，该平台设计用于合成具有在线反应分析的组合库。这种方法允许在加速时间段（192小时）内进行1000次反应。与完全连续方法相比，停止流动方法使用约10%的反应物和溶剂。这种方法通过简化交叉反应优化策略的实施，证明了较小文库的合成成功率显著提高。实验数据集用于训练前馈神经网络（FFNN）模型，该模型提供了指导进一步实验的框架，当用较少的实验对外部集进行测试时，该模型显示出良好的模型可预测性和成功性。因此，这项工作表明，将实验自动化与机器学习策略相结合可以提供优化的分析和增强的预测，从而在设计、制造、测试和分析（DMTA）周期中实现更有效的药物发现研究。将实验性停流自动化与机器学习策略相结合，可以提供优化的条件和增强的预测，从而实现更高效的设计、制造、测试和分析（DMTA）周期。
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. 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.
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影响因子：
7.3
作者：
Gioiello A;Piccinno A;Lozza AM;Cerra B
通讯作者：
Cerra B
影响因子：
2.7
作者：
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通讯作者：
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影响因子：
7.3
作者：
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作者：
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影响因子：
3.9
作者：
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通讯作者：
Bourne, Richard A.