Journal of Computer-Aided Molecular Design

, Volume 24, Issue 1, pp 49-56

First online:

Artificial neural network study on organ-targeting peptides

  • Eunkyoung JungAffiliated withInsilicotech Co. Ltd
  • , Junhyoung KimAffiliated withInsilicotech Co. Ltd
  • , Seung-Hoon ChoiAffiliated withInsilicotech Co. Ltd
  • , Minkyoung KimAffiliated withInsilicotech Co. Ltd
  • , Hokyoung RheeAffiliated withInsilicotech Co. Ltd
  • , Jae-Min ShinAffiliated withSBScience Co. Ltd
  • , Kihang ChoiAffiliated withDepartment of Chemistry, Korea University
  • , Sang-Kee KangAffiliated withSchool of Agriculture Biotechnology, Seoul National University
  • , Nam Kyung LeeAffiliated withSchool of Agriculture Biotechnology, Seoul National University
    • , Yun-Jaie ChoiAffiliated withSchool of Agriculture Biotechnology, Seoul National University
    • , Dong Hyun JungAffiliated withInsilicotech Co. Ltd Email author 

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We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.


Neural network Organ-targeting peptide ROC score VHSE descriptor