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A systematic DNN-based QSPR modeling methodology for rapid and reliable prediction on flashpoints of chemicals
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  • Huaqiang Wen,
  • Yang Su,
  • Zihao Wang,
  • saimeng Jin,
  • Jingzheng Ren,
  • Weifeng Shen,
  • Mario Eden
Huaqiang Wen
Chongqing University

Corresponding Author:[email protected]

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Yang Su
Chongqing University of Science and Technology
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Zihao Wang
Max Planck Institute for Dynamics of Complex Technical Systems
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saimeng Jin
Chongqing University
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Jingzheng Ren
The Hong Kong Polytechnic University
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Weifeng Shen
Chongqing University
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Mario Eden
Auburn University
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Quantitative structure-property relationship (QSPR) studies based on deep neural networks (DNN) are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to solve vital problems including applicability domain and prediction uncertainty in DNN-based QSPRs. 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 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. More importantly, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN-based QSPRs and presents an AD correlated with the uncertainty.
14 May 2021Submitted to AIChE Journal
19 May 2021Submission Checks Completed
19 May 2021Assigned to Editor
26 May 2021Reviewer(s) Assigned
15 Jun 2021Editorial Decision: Revise Major
06 Jul 20211st Revision Received
06 Jul 2021Submission Checks Completed
06 Jul 2021Assigned to Editor
13 Jul 2021Reviewer(s) Assigned
03 Aug 2021Editorial Decision: Accept