Artificial neural network study on organ-targeting peptides

Eunkyoung Jung, Junhyoung Kim, Seung Hoon Choi, Minkyoung Kim, Hokyoung Rhee, Jae Min Shin, Kihang Choi, Sang Kee Kang, Nam Kyung Lee, Yun Jaie Choi, Dong Hyun Jung

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)49-56
Number of pages8
JournalJournal of computer-aided molecular design
Issue number1
Publication statusPublished - 2010 Jan

Bibliographical note

Funding Information:
Acknowledgments This work was supported by the Korea Science and Engineering Foundation (KOSEF) NRL Program grant funded by the Korea government (MEST) (No. R0A-2008-000-20024-1).


  • Neural network
  • Organ-targeting peptide
  • ROC score
  • VHSE descriptor

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry


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