title: Prediction of Cell-Penetrating Peptides.
abstract: The in silico methods for the prediction of the cell-penetrating peptides are reviewed. Those include the multivariate statistical methods, machine-learning methods such as the artificial neural networks and support vector machines, and molecular modeling techniques including molecular docking and molecular dynamics.The applicability of the methods is demonstrated on the basis of the exemplary cases from the literature.
authors.affiliation: Department of Neurochemistry, Stockholm University, Stockholm, Sweden.
authors.forename: Mattias
authors.initials: M
authors.lastname: Hällbrink
authors.forename: Mati
authors.initials: M
authors.lastname: Karelson
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D057846:Cell-Penetrating Peptides / Q000737:chemistry*; D003198:Computer Simulation; D003205:Computing Methodologies; D006801:Humans; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D060388:Support Vector Machine
chemical_list: D057846:Cell-Penetrating Peptides