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- Title
Machine learning study for the prediction of transdermal peptide.
- Authors
Eunkyoung Jung; Seung-Hoon Choi; Nam Lee; Sang-Kee Kang; Yun-Jaie Choi; Jae-Min Shin; Kihang Choi; Dong Jung
- Abstract
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.
- Subjects
MACHINE learning; PEPTIDES; TRANSCUTANEOUS electrical nerve stimulation; ARTIFICIAL neural networks; SUPPORT vector machines; KERNEL functions
- Publication
Journal of Computer-Aided Molecular Design, 2011, Vol 25, Issue 4, p339
- ISSN
0920-654X
- Publication type
Article
- DOI
10.1007/s10822-011-9424-2