基于深度学习的溶剂定量构效关系建模研究进展

田璐瑶; 王梓豪; 粟杨; 文华强; 申威峰

doi:10.11949/0438-1157.20200814

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基于深度学习的溶剂定量构效关系建模研究进展

综述与专论 | 更新时间：2025-11-25

- 基于深度学习的溶剂定量构效关系建模研究进展
- Research advances in deep learning based quantitative structure-property relationship modeling of solvents
- 化工学报 2020年71卷第10期页码：4462-4472
- 作者机构：
  
  重庆大学化学化工学院，化工过程强化与反应国家地方联合工程实验室，重庆 401331
- 作者简介：
  
  田璐瑶（1996—），女，硕士研究生，tianluyao@cqu.edu.cn
  申威峰（1984—），男，博士，研究员，shenweifeng@cqu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(21878028);中央高校基本科研业务费项目(2019CDQYHG021)
- DOI：10.11949/0438-1157.20200814
  中图分类号： TQ 015.9
- 收稿：2020-06-22，
  
  修回：2020-07-24，
  
  纸质出版：2020-10-05
- 稿件说明：
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田璐瑶, 王梓豪, 粟杨, 文华强, 申威峰. 基于深度学习的溶剂定量构效关系建模研究进展[J]. 化工学报, 2020, 71(10): 4462-4472

TIAN Luyao, WANG Zihao, SU Yang, WEN Huaqiang, SHEN Weifeng. Research advances in deep learning based quantitative structure-property relationship modeling of solvents[J]. CIESC Journal, 2020, 71(10): 4462-4472
田璐瑶, 王梓豪, 粟杨, 文华强, 申威峰. 基于深度学习的溶剂定量构效关系建模研究进展[J]. 化工学报, 2020, 71(10): 4462-4472 DOI： 10.11949/0438-1157.20200814.

TIAN Luyao, WANG Zihao, SU Yang, WEN Huaqiang, SHEN Weifeng. Research advances in deep learning based quantitative structure-property relationship modeling of solvents[J]. CIESC Journal, 2020, 71(10): 4462-4472 DOI： 10.11949/0438-1157.20200814.

摘要

定量构效关系是溶剂分子设计与开发的重要理论基础，建立准确可靠的预测模型可以有效地解决性质数据库资源有限、实验过程人力物力消耗量大且具有危险性等问题。随着人工智能技术的快速发展，深度学习在化工领域取得突破性进展，基于此，综述了经典与智能化建模的研究理论与方法，重点介绍了基于深度学习实现大规模数据智能化关联的研究进展，进一步阐述了深度学习在有机物各种基础物性和环境健康安全等潜在影响性质预测中的潜力与优势，并从溶剂设计向绿色、安全、智能化发展的角度，展望了基于深度学习的定量构效关系在化学产品开发与化工过程设计等方面的理论研究方向和应用前景。

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.

关键词

Keywords

references

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