MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints

Shimeng Li, Li Zhang, Huawei Feng, Jinhui Meng, Di Xie, Liwei Yi, Isaiah T. Arkin, Hongsheng Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive models based on these representations. The predictive model based on features extracted by GCNNs can not only predict the mutagenicity of compounds but also identify the structure alerts in compounds. In fivefold cross-validation and external validation, the highest area under the curve was 0.8782 and 0.8382, respectively; the highest accuracy (Q) was 80.98% and 76.63%, respectively; the highest sensitivity was 83.27% and 78.92%, respectively; and the highest specificity was 78.83% and 76.32%, respectively. Additionally, our model also identified some toxicophores, such as aromatic nitro, three-membered heterocycles, quinones, and nitrogen and sulfur mustard. These results indicate that GCNNs could learn the features of mutagens effectively. In summary, we developed a mutagenicity classification model with high predictive performance and interpretability based on a data-driven molecular representation trained through GCNNs.

Original languageAmerican English
Pages (from-to)25-33
Number of pages9
JournalInterdisciplinary Sciences - Computational Life Sciences
Volume13
Issue number1
DOIs
StatePublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2021, International Association of Scientists in the Interdisciplinary Areas.

Keywords

  • Deep learning
  • Graph convolutional networks
  • Mutagenicity prediction

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