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DeepOCSR:一种用于光学化学结构识别的深度编码-解码网络

杨赵朋 李建华

杨赵朋, 李建华. DeepOCSR:一种用于光学化学结构识别的深度编码-解码网络[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210916002
引用本文: 杨赵朋, 李建华. DeepOCSR:一种用于光学化学结构识别的深度编码-解码网络[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210916002
YANG Zhaopeng, LI Jianhua. DeepOCSR: A Deep Encoder-Decoder Network for Optical Chemical Structure Recognition[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210916002
Citation: YANG Zhaopeng, LI Jianhua. DeepOCSR: A Deep Encoder-Decoder Network for Optical Chemical Structure Recognition[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210916002

DeepOCSR:一种用于光学化学结构识别的深度编码-解码网络

doi: 10.14135/j.cnki.1006-3080.20210916002
基金项目: 国家科技重大专项“现代中药组成资源库公共平台建设”(2018ZX09735002)
详细信息
    作者简介:

    杨赵朋(1996—),男,河南人,硕士生,主要研究方向为计算机视觉。E-mail:y30190800@mail.ecust.edu.cn

    通讯作者:

    李建华, E-mail:jhli@ecust.edu.cn

  • 中图分类号: TP391

DeepOCSR: A Deep Encoder-Decoder Network for Optical Chemical Structure Recognition

  • 摘要: 从科学出版物中识别光学化学结构是重新发现化学结构性质的重要组成部分,但基于规则的方法和新兴的深度学习方法都面临着识别率低的问题。本文提出了一种用于光学化学结构识别的深度学习方法(DeepOCSR)。该方法基于编码器-解码器架构,引入了Transformer和ResNeSt模型,将出版物中的化学结构图像转换为SMILES序列。构建了两种新的化学结构数据集,其中一个包含了化学文献中常见的取代基。将本文方法与现有的其他方法进行对比实验,结果表明本文方法在相似度和有效性等关键指标上均优于对比方法。

     

  • 图  1  基于DeepOCSR的化学结构识别框架

    Figure  1.  Chemical structure recognition framework based on DeepOCSR

    图  2  化学结构编码流程

    Figure  2.  Chemical structure encoding process

    图  3  DeepSMILES序列解码流程

    Figure  3.  DeepSMILES sequence decoding process

    图  4  生成化学结构图示例

    CC(C)C[C@@H](C(=O)O)NC(=O)N[C@@H]([C@@H]1CCN=C(N)N1)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C=O

    Figure  4.  Example of generating a chemical structure diagram

    图  5  CSDD-SUB数据集上DeepSMILES序列的长度分布

    Figure  5.  Distribution of lengths of DeepSMILES on CSDD-SUB dataset

    图  6  在CSDD-SUB 验证集上的损失值(a)和准确率(b)变化曲线

    Figure  6.  Loss (a) and accuracy (b) variation curves on CSDD-SUB validation set

    图  7  DeepOCSR和DECIMER在训练集上的收敛曲线

    Figure  7.  Convergence curves of DeepOCSR and DECIMER on training sets

    图  8  DeepOCSR在不同长度DeepSMILES的预测性能

    Figure  8.  Prediction performance of DeepOCSR on DeepSMILES with different lengths

    表  1  模型参数量和批处理时间的比较

    Table  1.   Comparison of model parameter numbers and batch processing times

    ModlParams/MBBatch time/s
    DA-LSTM58.250.71
    DeepOCSR64.160.64
    下载: 导出CSV

    表  2  不同堆叠层数时模型的性能比较结果

    Table  2.   Model performance comparison results with different number of stacking layers

    NAccuracySimilarityValidityBLEU
    288.650.9770.99430.9898
    490.250.9840.99430.9907
    691.710.9880.99580.9920
    下载: 导出CSV

    表  3  3种方法在两种测试集上的比较

    Table  3.   Comparison of three methods on two test sets

    Data setMethodAccuracySimilarityValidityBLEU
    CSDDMolVec47.190.8410.77750.6780
    DA-LSTM70.900.9170.99140.9423
    DeepOCSR82.260.9630.99400.9709
    CSDD-SUBMolVec17.030.4850.86470.5547
    DA-LSTM76.100.9590.95850.9657
    DeepOCSR91.710.9880.99580.9910
    下载: 导出CSV

    表  4  数据集划分

    Table  4.   Division of dataset

    Dataset indexMethodData size/ KBTrain data
    size
    Val data
    size
    Test data
    size
    1DECIMER605400006000
    DA-LSTM, DeepOCSR604800060006000
    2DECIMER10090000010000
    DA-LSTM, DeepOCSR100800001000010000
    3DECIMER500450000050000
    DA-LSTM, DeepOCSR5004000005000050000
    下载: 导出CSV

    表  5  3种方法在测试集上的性能比较

    Table  5.   Performance comparison of three approaches on testing set

    Dataset indexMethodEpochSimilarityValidity

    1
    DA-LSTM600.9280.9925
    DECIMER6000.3870.8992
    DeepOCSR600.9700.9977

    2
    DA-LSTM600.9600.9979
    DECIMER6000.3990.8913
    DeepOCSR600.9840.9992

    3
    DA-LSTM600.9900.9989
    DECIMER6000.4700.9805
    DeepOCSR600.9970.9996
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-16
  • 录用日期:  2021-12-31
  • 网络出版日期:  2022-04-15

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