title: Machine learning study for the prediction of transdermal peptide.
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.
authors: Insilicotech Co. Ltd., A-1101 Kolontripolis, 210 Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr
authors: Eunkyoung
authors: E
authors: Jung
authors: Seung-Hoon
authors: SH
authors: Choi
authors: Nam Kyung
authors: NK
authors: Lee
authors: Sang-Kee
authors: SK
authors: Kang
authors: Yun-Jaie
authors: YJ
authors: Choi
authors: Jae-Min
authors: JM
authors: Shin
authors: Kihang
authors: K
authors: Choi
authors: Dong Hyun
authors: DH
authors: Jung
mesh_terms: D000279:Administration, Cutaneous; D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D008297:Male; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000008:administration & dosage* / Q000737:chemistry*; D019151:Peptide Library; D051381:Rats; D017208:Rats, Wistar
chemical_list: D009842:Oligopeptides; D019151:Peptide Library