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高通量实验在信息丰富的化学合成中的应用。

Ultrahigh-Throughput Experimentation for Information-Rich Chemical Synthesis.

机构信息

Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.

出版信息

Acc Chem Res. 2021 May 18;54(10):2337-2346. doi: 10.1021/acs.accounts.1c00119. Epub 2021 Apr 23.

DOI:10.1021/acs.accounts.1c00119
PMID:33891404
Abstract

The incorporation of data science is revolutionizing organic chemistry. It is becoming increasingly possible to predict reaction outcomes with accuracy, computationally plan new retrosynthetic routes to complex molecules, and design molecules with sophisticated functions. Critical to these developments has been statistical analysis of reaction data, for instance with machine learning, yet there is very little reaction data available upon which to build models. Reaction data can be mined from the literature, but experimental data tends to be reported in a text format that is difficult for computers to read. Compounding the issue, literature data are heavily biased toward "productive" reactions, and few "negative" reaction data points are reported even though they are critical for training of statistical models. High-throughput experimentation (HTE) has evolved over the past few decades as a tool for experimental reaction development. The beauty of HTE is that reactions are run in a systematic format, so data points are internally consistent, the reaction data are reported whether the desired product is observed or not, and automation may reduce the occurrence of false positive or negative data points. Additionally, experimental workflows for HTE lead to datasets with reaction metadata that are captured in a machine-readable format. We believe that HTE will play an increasingly important role in the data revolution of chemical synthesis. This Account details the miniaturization of synthetic chemistry culminating in ultrahigh-throughput experimentation (ultraHTE), wherein reactions are run in ∼1 μL droplets inside of 1536-well microtiter plates to minimize the use of starting materials while maximizing the output of experimental information. The performance of ultraHTE in 1536-well microtiter plates has led to an explosion of available reaction data, which have been used to identify specific substrate-catalyst pairs for maximal efficiency in novel cross-coupling reactions. The first iteration of ultraHTE focused on the use of dimethyl sulfoxide (DMSO) as a high-boiling solvent that is compatible with the plastics most commonly used in consumable well plates, which generated homogeneous reaction mixtures that are perfect for use with nanoliter-dosing liquid handling robotics. In this way, DMSO enabled diverse reagents to be arrayed in ∼1 μL droplets. Reactions were run at room temperature with no agitation and could be scaled up from the ∼0.05 mg reaction scale to the 1 g scale. Engineering enhancements enabled the use of ultraHTE with diverse and semivolatile solvents, photoredox catalysis, heating, and acoustic agitation. A main driver in the development of ultraHTE was the recognition of the opportunity for a direct merger between miniaturized reactions and biochemical assays. Indeed, a strategy was developed to feed ultraHTE reaction mixtures directly to a mass-spectrometry-based affinity selection bioassay. Thus, micrograms of starting materials could be used in the synthesis and direct biochemical testing of drug-like molecules. Reactions were performed at a reactant concentration of ∼0.1 M in an inert atmosphere, enabling even challenging transition-metal-catalyzed reactions to be used. Software to enable the workflow was developed. We recently initiated the mapping of reaction space, dreaming of a future where transformations, reaction conditions, structure, properties and function are studied in a systems chemistry approach.

摘要

数据科学的融入正在彻底改变有机化学。现在,我们越来越有可能准确地预测反应结果,通过计算规划新的复杂分子反合成路线,并设计具有复杂功能的分子。这些发展的关键是对反应数据进行统计分析,例如使用机器学习,但几乎没有可用的反应数据来构建模型。反应数据可以从文献中挖掘,但实验数据往往以文本格式报告,计算机难以读取。更糟糕的是,文献数据严重偏向于“有生产力”的反应,尽管对于统计模型的训练来说,“负面”反应数据点至关重要,但很少有报告。高通量实验(HTE)在过去几十年中作为一种实验反应开发工具而发展起来。HTE 的美妙之处在于反应是按照系统的格式进行的,因此数据点是一致的,无论是否观察到所需产物,都会报告反应数据,并且自动化可以减少假阳性或假阴性数据点的出现。此外,HTE 的实验工作流程会生成具有反应元数据的数据集,这些数据以机器可读的格式捕获。我们相信,HTE 将在化学合成的数据革命中发挥越来越重要的作用。本账户详细介绍了合成化学的微型化,最终达到超高通量实验(ultraHTE),其中反应在 1536 孔微量滴定板中的约 1 μL 液滴中进行,以最小化起始材料的使用量,同时最大限度地提高实验信息的输出。ultraHTE 在 1536 孔微滴定板中的性能导致了可用反应数据的爆炸式增长,这些数据已被用于确定特定的底物-催化剂对,以实现新型交叉偶联反应的最大效率。ultraHTE 的第一个迭代侧重于使用二甲基亚砜(DMSO)作为高沸点溶剂,它与最常用于消耗性微孔板的塑料兼容,可生成均质的反应混合物,非常适合使用纳升剂量的液体处理机器人。通过这种方式,DMSO 使各种试剂能够排列在约 1 μL 的液滴中。反应在室温下进行,无需搅拌,可从 0.05mg 的反应规模扩大到 1g 的规模。工程改进使 ultraHTE 能够与各种半挥发性溶剂、光还原催化、加热和声波搅拌一起使用。ultraHTE 发展的一个主要驱动因素是认识到微型化反应和生化分析之间直接融合的机会。事实上,已经开发出一种策略,可以将 ultraHTE 反应混合物直接输送到基于质谱的亲和选择生物测定。因此,可以使用微克的起始材料进行药物样分子的合成和直接生化测试。反应在惰性气氛中以约 0.1M 的反应物浓度进行,即使是具有挑战性的过渡金属催化反应也可以使用。开发了用于实现该工作流程的软件。我们最近开始了反应空间的映射,梦想着有一天,转化、反应条件、结构、性质和功能能够以系统化学的方法进行研究。

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