title: Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.
abstract: An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.
authors.affiliation: Department of Chemistry,Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086,Estonia. dimitar@molcode.com.
authors.forename: Dimitar A
authors.initials: DA
authors.lastname: Dobchev
authors.forename: Imre
authors.initials: I
authors.lastname: Mager
authors.forename: Indrek
authors.initials: I
authors.lastname: Tulp
authors.forename: Gunnar
authors.initials: G
authors.lastname: Karelson
authors.forename: Tarmo
authors.initials: T
authors.lastname: Tamm
authors.forename: Kaido
authors.initials: K
authors.lastname: Tamm
authors.forename: Jaak
authors.initials: J
authors.lastname: Janes
authors.forename: Ulo
authors.initials: U
authors.lastname: Langel
authors.forename: Mati
authors.initials: M
authors.lastname: Karelson
mesh_terms: D000818:Animals; D057846:Cell-Penetrating Peptides / Q000493:pharmacokinetics*; D002477:Cells / Q000378:metabolism*; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
chemical_list: D057846:Cell-Penetrating Peptides