Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.

Journal article

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: Dobchev, D; Mäger, I; Tulp, I; Karelson, G; Tamm, T

• Journal: Current computer-aided drug design
• Volume: 6
• Issue: 2
• Pages: 79-89
• Publication date: 2010-04-01
• DOI: 10.2174/157340910791202478
• EISSN: 1875-6697
• ISSN: 1573-4099
• Language: English
• Keywords: QSAR; Artificial neural networks (ANN); PCA; Cell-penetrating peptides (CPP)