Machine learning study for the prediction of transdermal peptide

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

Original languageEnglish
Pages (from-to)339-347
Number of pages9
JournalJournal of computer-aided molecular design
Issue number4
Publication statusPublished - 2011 Apr


  • Artificial neural network
  • Machine learning
  • Partial least squares
  • ROC score
  • Support vector machine
  • Transdermal peptide
  • VHSE descriptor

ASJC Scopus subject areas

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


Dive into the research topics of 'Machine learning study for the prediction of transdermal peptide'. Together they form a unique fingerprint.

Cite this