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Molecular Bioactivity Prediction of HDAC1: Based on Deep Neural Nets

In: AI and Analytics for Public Health

Author

Listed:
  • Miaomiao Chen

    (Nanjing University of Aeronautics and Astronautics)

  • Shan Li

    (Nanjing University of Aeronautics and Astronautics)

  • Yu Ding

    (Nanjing University of Aeronautics and Astronautics)

  • Hongwei Jin

    (Peking University)

  • Jie Xia

    (Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College)

Abstract

With the important impact on the research and treatment of tumors and cardiovascular diseases, histone deacetylases1 (HDAC1) has been a hot target recently. In this work, we extracted three kinds of relevant features (molecular descriptors, MACCS and ESTATE fingerprints) from the simplified molecular input line entry specification (SMILES) of HDAC1 molecules, and then established separate predicting models based on deep neural networks. All of the models performed well in predicting the activity of the test set. But with regard to 7 modeling metrics, we found that, in the overall predictive performance and the ability to identify inactive molecules, the model trained with MACCS fingerprints had obvious advantages (the AUC value is up to 0.9). As for the identification of active molecules, the model trained with molecular descriptors performed best. The results provide a reference for feature selection when constructing quantitative structure-activity relationships (QSAR) of inhibitor drugs like HDAC1 based on DNNs.

Suggested Citation

  • Miaomiao Chen & Shan Li & Yu Ding & Hongwei Jin & Jie Xia, 2022. "Molecular Bioactivity Prediction of HDAC1: Based on Deep Neural Nets," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 229-240, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_15
    DOI: 10.1007/978-3-030-75166-1_15
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