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

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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.

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Author notes

  1. Shimeng Li and Li Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Life Science, Liaoning University, Shenyang, 110036, China

    Shimeng Li, Li Zhang, Huawei Feng, Jinhui Meng, Di Xie & Hongsheng Liu

  2. Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, 110036, China

    Li Zhang & Hongsheng Liu

  3. Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China

    Li Zhang & Hongsheng Liu

  4. School of Information, Liaoning University, Shenyang, 110036, China

    Liwei Yi

  5. Department of Biological Chemistry, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat-Ram, 91904, Jerusalem, Israel

    Isaiah T. Arkin

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  1. Shimeng Li
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Correspondence to Hongsheng Liu.

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Li, S., Zhang, L., Feng, H. et al. MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints. Interdiscip Sci Comput Life Sci 13, 25–33 (2021). https://doi.org/10.1007/s12539-020-00407-2

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