An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Yang Su, Zihao Wang, Saimeng Jin, Weifeng Shen, Jingzheng Ren, Mario R. Eden

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


Deep learning rapidly promotes many fields with successful stories in natural language processing. An architecture of deep neural network (DNN) combining tree-structured long short-term memory (Tree-LSTM) network and back-propagation neural network (BPNN) is developed for predicting physical properties. Inspired by the natural language processing in artificial intelligence, we first developed a strategy for data preparation including encoding molecules with canonical molecular signatures and vectorizing bond-substrings by an embedding algorithm. Then, the dynamic neural network named Tree-LSTM is employed to depict molecular tree data-structures while the BPNN is used to correlate properties. To evaluate the performance of proposed DNN, the critical properties of nearly 1,800 compounds are employed for training and testing the DNN models. As compared with classical group contribution methods, it can be demonstrated that the learned DNN models are able to provide more accurate prediction and cover more diverse molecular structures without considering frequencies of substructures.

Original languageEnglish
Article numbere16678
JournalAICHE Journal
Issue number9
Publication statusPublished - Sep 2019


  • critical properties
  • deep learning
  • neural network
  • property prediction
  • signature molecular descriptor

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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