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有机合成中化学反应的机器学习

张良顺

张良顺. 有机合成中化学反应的机器学习[J]. 功能高分子学报. doi: 10.14133/j.cnki.1008-9357.20210823002
引用本文: 张良顺. 有机合成中化学反应的机器学习[J]. 功能高分子学报. doi: 10.14133/j.cnki.1008-9357.20210823002
ZHANG Liangshun. Machine Learning on the Chemical Reaction of Organic Synthesis[J]. Journal of Functional Polymers. doi: 10.14133/j.cnki.1008-9357.20210823002
Citation: ZHANG Liangshun. Machine Learning on the Chemical Reaction of Organic Synthesis[J]. Journal of Functional Polymers. doi: 10.14133/j.cnki.1008-9357.20210823002

有机合成中化学反应的机器学习

doi: 10.14133/j.cnki.1008-9357.20210823002
详细信息
    作者简介:

    张良顺(1981—),男,博士,主要研究方向为高分子理论与模拟。E-mail:zhangls@ecust.edu.cn

  • 中图分类号: O414;O552

Machine Learning on the Chemical Reaction of Organic Synthesis

  • 摘要: 化学反应预测及合成路线设计是有机合成领域极具挑战性的问题之一。机器学习是近来新兴的研究方法。针对有机小分子的化学合成,本文综述了机器学习方法在有机合成(包括化学反应数据的收集、化学反应的预测和合成路线的设计等)领域的进展。对于高度复杂的树脂分子,本文论述了基于机器学习合成路线亟待解决的问题,如数据的不完备和偏倚、缺乏规范化的表示、少样本的机器学习方法等。

     

  • 图  1  化学反应数据收录流程图

    Figure  1.  Schematic of the dataset collection of chemical reaction

    图  2  化学反应预测模型的示意图(插图为全连接神经网络)[12]

    Figure  2.  Schematic of machine-learning model for the prediction of chemical reaction(Inset shows the fully connected network)[12]

    图  3  化学反应预测的图卷积神经网络模型[15]

    Figure  3.  Modelling of graph-convolution nerve networks for the prediction of chemical reaction[15]

    图  4  逆合成反应预测示意图(插图为Seq2seq模型)[21]

    Figure  4.  Schematic of machine-learning model for the retrosynthesis(Inset shows the Seq2seq model)[21]

  • [1] BLUROCK, E S. Reaction: System for modeling chemical reactions [J]. Journal of Chemical Information and Computer Sciences,1995,35(3):607-616. doi: 10.1021/ci00025a032
    [2] LIAO R Z, THIEL W. Comparison of QM-only and QM/MM models for the mechanism of Tungsten- dependent acetylene hydratase [J]. Journal of Chemical Theory and Computation,2012,8(10):3793-3803. doi: 10.1021/ct3000684
    [3] COLEY C W, GREEN W H, JENSEN K F. Machine learning in computer-aided synthesis planning [J]. Accounts of Chemical Research,2018,51(5):1281-1289. doi: 10.1021/acs.accounts.8b00087
    [4] PEIRETTI F, BRUNEL J M. Artificial intelligence: The future for organic chemistry? [J]. ACS Omega,2018,3(10):13263-13266. doi: 10.1021/acsomega.8b01773
    [5] COLEY C W, EYKE N S, JENSEN K F. Autonomous discovery in the chemical sciences part I: Progress [J]. Angewandte Chemie International Edition,2020,59(52):22858-22893.
    [6] TSHITOYAN V, DAGDELEN J, WESTON L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature [J]. Nature, 2019, 571(7763): 95-98.
    [7] LOWE D M. Extraction of chemical structures and reactions from the literature [D]. University of Cambridge, 2012.
    [8] COLEY C W, EYKE N S, JENSEN K F. Autonomous discovery in the chemical sciences part II: Outlook [J]. Angewandte Chemie International Edition,2020,59(52):23414-23436. doi: 10.1002/anie.201909989
    [9] WEI J N, DUVENAUD D, ASPURU-GUZIK A. Neural networks for the prediction of organic chemistry reactions [J]. ACS Central Science,2016,2(10):725-732. doi: 10.1021/acscentsci.6b00219
    [10] SEGLER M, KOGEJ T, TYRCHAN C, et al. Generating focused molecule libraries for drug discovery with recurrent neural networks [J]. ACS Central Science,2018,4(1):120-131. doi: 10.1021/acscentsci.7b00512
    [11] SEGLER W H, PREUSS M, WALLER M. Planning chemical syntheses with deep neural networks and symbolic AI [J]. Nature,2018,555(7698):604-610. doi: 10.1038/nature25978
    [12] COLEY C W, BARZILAY R, JAAKKOLA T S, et al. Prediction of organic reaction outcomes using machine learning [J]. ACS Central Science,2017,3(5):434-443. doi: 10.1021/acscentsci.7b00064
    [13] SCHWALLER P, LAINO T, GAUDIN T, et al. Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction [J]. ACS Central Science,2019,5(9):1572-1583. doi: 10.1021/acscentsci.9b00576
    [14] SCHWALLER P, GAUDIN T, Lanyi D, et al. "Found in Translation": Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models [J]. Chemical Science,2018,9(28):6091-6098. doi: 10.1039/C8SC02339E
    [15] COLEY C W, JIN W, ROGERS L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity [J]. Chemical Science,2019,10(2):370-377. doi: 10.1039/C8SC04228D
    [16] GUAN Y, COLEY C W, WU H, et al. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors [J]. Chemical Science,2021,12(6):2198-2208. doi: 10.1039/D0SC04823B
    [17] MOHAPATRA S, HARTRAMPF N, POSKUS, et al. Deep learning for prediction and optimization of fast-flow peptide synthesis [J]. ACS Central Science,2020,6(12):2277-2286. doi: 10.1021/acscentsci.0c00979
    [18] GAO H, STRUBLE T J, COLEY C W, et al. Using machine learning to predict suitable conditions for organic reactions [J]. ACS Central Science,2018,4(11):1465-1476. doi: 10.1021/acscentsci.8b00357
    [19] ZHOU Z, LI X, ZARE R. Optimizing chemical reactions with deep reinforcement learning [J]. ACS Central Science,2017,3(12):1337-1344. doi: 10.1021/acscentsci.7b00492
    [20] SHIELDS B J, STEVENS J, LI J, et al. Bayesian reaction optimization as a tool for chemical synthesis [J]. Nature,2021,590(7844):89-96. doi: 10.1038/s41586-021-03213-y
    [21] LIU B, RAMSUNDAR B, KAWTHEKAR P, et al. Retrosynthetic reaction prediction using neural sequence-to- sequence models [J]. ACS Central Science,2017,3(10):1103-1113. doi: 10.1021/acscentsci.7b00303
    [22] ZHENG S, RAO J, ZHANG Z, et al. Predicting retrosynthetic reactions using self-corrected transformer neural networks [J]. Journal of Chemical Information and Modeling,2020,60(1):47-55. doi: 10.1021/acs.jcim.9b00949
    [23] MO Y, GUAN Y, VERMA P, et al. Evaluating and clustering retrosynthesis pathways with learned strategy [J]. ACS Central Science,2021,12(4):1469-1478.
    [24] SCHWALLER P, PETRAGLIA R, ZULLO V, et al. Predicting retrosynthetic pathways using transformer- based models and a hyper-graph exploration strategy [J]. Chemical Science,2020,11(12):3316-3325. doi: 10.1039/C9SC05704H
    [25] SHIBUKAWA R, ISHIDA S, YOSHIZOE K, et al. CompRet: A comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration [J]. Journal of Cheminformatics,2020,12(52):1-14.
    [26] COLEY C W, ROGERS L, GREEN W H, et al. SCScore: Synthetic complexity learned from a reaction corpus [J]. Journal of Chemical Information and Modeling,2018,58(2):252-261. doi: 10.1021/acs.jcim.7b00622
    [27] LIN T, COLEY C W, MOCHIGASE H, et al. BigSMILES: A structurally-based line notation for describing macromolecules [J]. ACS Central Science,2019,5(9):1523-1531. doi: 10.1021/acscentsci.9b00476
    [28] ZHAO S, CAI T, ZHANG L, et al. Autonomous construction of phase diagrams of block copolymers by theory-assisted active machine learning [J]. ACS Macro Letters,2021,10(5):598-602. doi: 10.1021/acsmacrolett.1c00133
    [29] HASE F, ROCH L M, ASPURU-GUZIK A. Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories [J]. Chemical Science,2018,9(39):7642-7655. doi: 10.1039/C8SC02239A
    [30] COLEY C W, THOMAS III D A, LUMMISS J A, et al. A robotic platform for flow synthesis of organic compounds informed by AI planning [J]. Science,2019,365(557):eaax1566.
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出版历程
  • 收稿日期:  2021-08-23
  • 网络出版日期:  2021-09-22

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