化工学报 ›› 2020, Vol. 71 ›› Issue (10): 4462-4472.DOI: 10.11949/0438-1157.20200814
收稿日期:
2020-06-22
修回日期:
2020-07-24
出版日期:
2020-10-05
发布日期:
2020-10-05
通讯作者:
申威峰
作者简介:
田璐瑶(1996—),女,硕士研究生,基金资助:
Luyao TIAN(),Zihao WANG,Yang SU,Huaqiang WEN,Weifeng SHEN()
Received:
2020-06-22
Revised:
2020-07-24
Online:
2020-10-05
Published:
2020-10-05
Contact:
Weifeng SHEN
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。
中图分类号:
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.
Luyao TIAN, Zihao WANG, Yang SU, Huaqiang WEN, Weifeng SHEN. Research advances in deep learning based quantitative structure-property relationship modeling of solvents[J]. CIESC Journal, 2020, 71(10): 4462-4472.
方法 | 研究对象 | 文献 |
---|---|---|
深度信念网络(DBN) | 抗HIV活性 | [ |
递归神经网络(RNN) | 药物分子的水溶性 | [ |
卷积神经网络(CNN) | 毒性、活性和溶剂化性质 | [ |
长短期记忆-卷积神经 网络(LSTM-CNN) | 药物分子的毒性和 活性 | [ |
表1 基于深度学习的定量构效关系研究
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 | [ |
递归神经网络(RNN) | 药物分子的水溶性 | [ |
卷积神经网络(CNN) | 毒性、活性和溶剂化性质 | [ |
长短期记忆-卷积神经 网络(LSTM-CNN) | 药物分子的毒性和 活性 | [ |
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