A systematic modeling methodology of deep neural network-based structure-property relationship for rapid and reliable prediction on flashpoints

Huaqiang Wen, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Weifeng Shen, Mario Eden

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR) studies are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to systematically solve vital problems including applicability domain and prediction uncertainty in DNN-based QSPR modeling. Key features are rapidly extracted from plentiful but chaotic descriptors by principal component analysis (PCA) and kernel PCA. Then, a detailed applicability domain (AD) is defined by K-means algorithm to avoid unreliable predictions and discover its potential impact on prediction uncertainty. Moreover, prediction uncertainty is analyzed with dropout-embedded DNN by thousands of independent tests to assess the reliability of predictions. The prediction of flashpoint temperature is employed as a case study, demonstrating that the model accuracy is remarkably improved comparing with the referenced model. Furthermore, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN-based QSPRs and presents an AD correlated with the uncertainty.

Original languageEnglish
Article numbere17402
Number of pages15
JournalAICHE Journal
Issue number1
Publication statusPublished - Jan 2022


  • deep neural network
  • flashpoint
  • principal component analysis
  • QSPR
  • uncertainty analysis

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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