Predicting the structure of unexplored novel fentanyl analogues by deep learning model.

Journal: Briefings in bioinformatics
PMID: 36184256

Abstract

Fentanyl and its analogues are psychoactive substances and the concern of fentanyl abuse has been existed in decades. Because the structure of fentanyl is easy to be modified, criminals may synthesize new fentanyl analogues to avoid supervision. The drug supervision is based on the structure matching to the database and too few kinds of fentanyl analogues are included in the database, so it is necessary to find out more potential fentanyl analogues and expand the sample space of fentanyl analogues. In this study, we introduced two deep generative models (SeqGAN and MolGPT) to generate potential fentanyl analogues, and a total of 11 041 valid molecules were obtained. The results showed that not only can we generate molecules with similar property distribution of original data, but the generated molecules also contain potential fentanyl analogues that are not pretty similar to any of original data. Ten molecules based on the rules of fentanyl analogues were selected for NMR, MS and IR validation. The results indicated that these molecules are all unreported fentanyl analogues. Furthermore, this study is the first to apply the deep learning to the generation of fentanyl analogues, greatly expands the exploring space of fentanyl analogues and provides help for the supervision of fentanyl.

Authors

  • Yuan Zhang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qiaoyan Jiang
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
  • Ling Li
    College of Communication Engineering, Jilin University, Changchun, Jilin China.
  • Zutan Li
    College of Agriculture, Nanjing Agricultural University, Jiangsu, 210095, Nanjing, China.
  • Zhihui Xu
    Researcher in Simcere Diagnostics Co., Ltd, China.
  • Yuanyuan Chen
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Yang Sun
    Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Cheng Liu
    Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.
  • Zhengsheng Mao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China. maozhengsheng@njmu.edu.cn.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Hualan Li
    Bioinformatics Master Student at Nanjing Agricultural University, China.
  • Yue Cao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
  • Cong Pian
    1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.

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