Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism

Yanan Tian, Xiaorui Wang, Xiaojun Yao, Huanxiang Liu, Ying Yang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Graph neural networks based on deep learning methods have been extensively applied to the molecular property prediction because of its powerful feature learning ability and good performance. However, most of them are black boxes and cannot give the reasonable explanation about the underlying prediction mechanisms, which seriously reduce people's trust on the neural network-based prediction models. Here we proposed a novel graph neural network named iteratively focused graph network (IFGN), which can gradually identify the key atoms/groups in the molecule that are closely related to the predicted properties by the multistep focus mechanism. At the same time, the combination of the multistep focus mechanism with visualization can also generate multistep interpretations, thus allowing us to gain a deep understanding of the predictive behaviors of the model. For all studied eight datasets, the IFGN model achieved good prediction performance, indicating that the proposed multistep focus mechanism also can improve the performance of the model obviously besides increasing the interpretability of built model. For researchers to use conveniently, the corresponding website (http://graphadmet.cn/works/IFGN) was also developed and can be used free of charge.

Original languageEnglish
Article numberbbac534
JournalBriefings in Bioinformatics
Volume24
Issue number1
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

  • deep learning
  • graph neural networks
  • interpretability
  • molecular property prediction
  • multistep focus mechanism

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