CIESC Journal ›› 2020, Vol. 71 ›› Issue (10): 4462-4472.doi: 10.11949/0438-1157.20200814

• Reviews and monographs • Previous Articles     Next Articles

Research advances in deep learning based quantitative structure-property relationship modeling of solvents

Luyao TIAN(),Zihao WANG,Yang SU,Huaqiang WEN,Weifeng SHEN()   

  1. National-Municipal Joint Engineering Laboratory for Chemical Process Intensification and Reaction, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China
  • Received:2020-06-22 Revised:2020-07-24 Online:2020-10-05 Published:2020-08-15
  • Contact: Weifeng SHEN E-mail:tianluyao@cqu.edu.cn;shenweifeng@cqu.edu.cn

Abstract:

Quantitative structure-property relationship is an important theoretical basis for the design and development of solvent molecules. The establishment of an accurate and reliable prediction model can effectively solve the problems of limited property database resources, large human and material resources consumption and dangerousness in the experimental process. With the rapid development of artificial intelligence technology, deep learning has made some breakthroughs in chemical industry. In this context, this work reviews the research theories and methods of classical and intelligent modeling, and introduces some advances of deep learning in intelligent modeling on large-scale data. In addition, the advantages and application prospects of deep learning techniques in the prediction of various basic physical properties as well as potential impacts on environment, health and safety of organics are elaborated. From the angle of the intelligent development of green solvents, the prospects of theoretical and application researches on quantitative structure-property relationship based on deep learning are outlined in the development of chemical product and process.

Key words: deep learning, solvent, structure-property relationship, product design, predictive model, neural network

CLC Number: 

  • TQ 015.9

Fig.1

Ethanol molecule represented using the group contribution method"

Fig.2

Property prediction of compounds using the artificial neural network"

Fig.3

Difference between classic machine learning methods and deep learning techniques in predictive modeling"

Table 1

Studies of deep learning based quantitative structure-property relationship"

方法研究对象文献
深度信念网络(DBN)抗HIV活性[53]
递归神经网络(RNN)药物分子的水溶性[54]
卷积神经网络(CNN)毒性、活性和溶剂化性质[55]
长短期记忆-卷积神经 网络(LSTM-CNN)药物分子的毒性和 活性[56]

Fig.4

Development of predictive models for structure-property relationships based on the deep learning"

Fig.5

Analyses of the deep learning based predictive model of the structure-property relationship"

Fig.6

Framework of the multitask deep learning neural network"

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