TY  - JOUR
ID  - open1763
UR  - http://download.springer.com/static/pdf/217/chp%253A10.1007%252F978-1-4939-2806-4_4.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fprotocol%2F10.1007%2F978-1-4939-2806-4_4&token2=exp=1454040805~acl=%2Fstatic%2Fpdf%2F217%2Fchp%25253A10.1007%25252F978-1-493
A1  - Gautam, Ankur
A1  - Chaudhary, Kumardeep
A1  - Kumar, Rahul
A1  - Raghava, G.P.S.
Y1  - 2015///
N2  - Cell-penetrating peptides (CPPs) have proven their potential as versatile drug delivery vehicles. Last decade has witnessed an unprecedented growth in CPP-based research, demonstrating the potential of CPPs as therapeutic candidates. In the past, many in silico algorithms have been developed for the prediction and screening of CPPs, which expedites the CPP-based research. In silico screening/prediction of CPPs followed by experimental validation seems to be a reliable, less time-consuming, and cost-effective approach. This chapter describes the prediction, screening, and designing of novel efficient CPPs using "CellPPD," an in silico tool.
PB  - Springer
JF  - Methods in molecular biology (Clifton, N.J.)
VL  - 1324
KW  - Cell-penetrating peptides ? Drug delivery system ? Machine learning approach ? Virtual screening ? Support vector machine ? Prediction
SN  - 1940-6029
TI  - Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides.
SP  - 59
EP  - 69
ER  -