Application of automation and artificial intelligence in flow chemistry

doi:10.11949/0438-1157.20240836

Abstract

Abstract:

Flow chemistry, as an important frontier technology in chemical synthesis, has the characteristics of high efficiency, safety, controllability, and environmental protection. It is widely used in drug synthesis, fine chemicals, and materials science. Recent advances in automation and artificial intelligence (AI) have enabled flow chemistry to achieve full-process automation and intelligence. Automated systems integrate equipment and control systems to monitor and regulate reaction conditions in real-time, minimizing human error and enhancing stability and reproducibility. AI algorithms allow these systems to handle large data volumes, navigate high-dimensional chemical space, shorten experimental cycles, and improve efficiency and yield. This paper summarizes the development of intelligent flow chemistry systems, discusses the construction and application of the automated systems, and explores AI-based advancements, including machine learning algorithms and large language models. Case studies highlight the significant potential and application prospects of intelligent flow chemistry systems in chemical synthesis.

Key words: flow chemistry, automation, artificial intelligence, high-throughput screening, machine learning

摘要：

流动化学作为化学合成的重要前沿技术，具有高效、安全可控、环保等特点，广泛应用于药物合成、精细化工和材料科学等多个领域。近年来，随着自动化与人工智能技术的发展，流动化学逐渐实现了全流程自动化和智能化的系统转型。自动化流动化学系统通过集成自动化设备与控制系统，实时监督与调控反应条件，尽可能减少人为操作带来的误差，从而提高实验稳定性和可重复性。人工智能算法的引入使系统能够处理大量文献及实验数据，探索高维化学空间，缩短实验周期，提升反应效率和发现新反应。初步总结了智能化流动化学系统的发展现状，介绍了自动化流动化学系统的构建与应用，同时探讨基于人工智能的智能化发展路径，包括机器学习算法和大语言模型的应用等。基于实例分析现阶段的研究成果，展示了智能化流动化学系统在化学合成中的巨大潜力与应用前景。

关键词: 流动化学, 自动化, 智能化, 高通量筛选, 机器学习

CLC Number:

TQ 028.8

Jian RUAN, Shuang LI, Zhenghui WEN. Application of automation and artificial intelligence in flow chemistry[J]. CIESC Journal, 2024, 75(11): 4120-4140.

阮见, 李双, 温正慧. 自动化与智能化在流动化学中的应用[J]. 化工学报, 2024, 75(11): 4120-4140.

Figures/Tables 21

Fig.1 The first automated solid-phase polypeptide synthesis equipment[23]

Fig.2 The basic function module node of the automated flow chemistry platform

Fig.3 The user interface of the automated platform. (a) LabView-based SPS-flow synthesis platform user interface[31]; (b) LabView and MATLAB-based High-throughput electrochemical microfluidic platform user interface[32]; (c) Python-based RoboChem platform user interface[35]; (d) Python-based NIMS-OS platform user interface[37]

Fig.4 Control module of automation platform[19]

Fig.5 Reactors for different reaction platforms: (a) Photocatalytic reactor[40]; (b) Electrocatalytic reactor[58]

Table 1 Comparison of common online analysis device connection mode and advantages/disadvantages［68］

仪器类型	连接方式	优点	缺点	响应时间
红外光谱（IR）	in-line	快速数据获取非破坏性检测能分析气体、液体和固体样品	提供的结构信息有限对高温、高压的适应性较差	2 s~2 min
紫外/可见吸收光谱（UV-vis）	in-line	高灵敏度能快速进行定性和定量分析	对光敏感试剂可能产生影响提供的结构信息较少	2~30 s
核磁共振波谱（NMR）	in-line on-line	非破坏性检测提供详细的结构信息能检测反应中的瞬态中间体分析速度快	设备成本高	1 min~2 h
高效液相色谱（HPLC）	at-line	提供深入的样品分析适用于复杂混合物高灵敏度	需要标定过程需要样品前处理分析时间相对较长	5~ 30 min
气相色谱（GC）	at-line	高分辨率快速分析	需要标定过程仅限于挥发性和热稳定性化合物需要样品前处理	5~30 min
质谱（MS）	at-line	高灵敏度分析速度快适用范围广能提供详细的结构信息	设备成本高破坏性检测	1 s~2 min
拉曼光谱（Raman）	in-line	非破坏性检测分析速度快适用于含水体系	灵敏度低提供的结构化信息少	1~30 s

Fig.6 Summary of automated inline separation: (a) automated inline nanofiltration to recycle homogeneous catalyst[69]; (b) automated continuous crystallization[74]; (c) automated inline flash chromatography[77]

Fig.7 Continuous flow automated system: (a) continuous flow microreactor automation platform[78]; (b) the platform for automated nanomolescale reaction screening and micromole-scale synthesis in flow[62]

Fig.8 Automated droplet-based microfluidic high-throughput photochemical reaction discovery platform[56]

Fig.9 Automated continuous-flow electrochemical platform: (a) multifunctional microfluidic platform for high-throughput experimentation of electrochemistry[32]; (b) automated slug-based electrochemical flow platform[80]

Fig.10 Automated flow peptide synthesis experimental platform: (a) automated flow synthesis of proteins[81]; (b) automated solid-phase synthesis of PMOs[82]

Fig.11 Automated systems for multistep synthesis: (a) automated switching-valve-based continuous-flow system, AutoSyn[70]; (b) radial synthesis platform[83]; (c) automated continuous-flow solid phase synthesis platform[31]

Fig.12 DoE method used to optimize flow synthesis process. (a) Synthesis of 1,2,4-oxadiazole and 1,2,4-triazole small molecule compound libraries[88]; (b) Synthesis of the fused pyrimidinone and quinolone derivatives under high temperature and pressure[90]

Fig.13 Nelder-Mead simplex-based optimization of imine synthesis reaction[92]

Fig.14 ‘Plug-and-play ’ automated flow platform with SNOBFIT strategy[30]

Fig.15 The C—N cross-coupling reaction study based on random forest algorithm and multi-armed bandit algorithm[33,97]

Fig.16 Bayesian optimization-based intelligent synthetic system. (a) Modular robot flow synthesis platform[100]; (b) RoboChem, a closed-loop, multi-objective optimization platform for photocatalysis system[35]; (c) Multi-task Bayesian optimization synthesis platform[102]

Fig.17 Intelligent flow chemistry system for exploring the new synthetic path of small molecule drug[19]

Fig.18 Allchemy: a computational platform for forward synthesis in complex molecule design and synthesis[103]

Fig.19 LLM-based automated chemical platform: (a) Coscientist[106]; (b) ChemCrow[107]

Fig.20 The preliminary applications of automated experiment platform in cloud laboratory field: (a) cross-border cloud laboratory systems[111]; (b) the cloud platform coordinates multi-laboratory discovery and optimization of organic laser emitters[34]

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