Artificial neural network models for prediction of intestinal permeability of oligopeptides

Background: Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results: The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) c...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Eunkyoung Jung, Junhyoung Kim, Minkyoung Kim, Dong Hyun Jung, Hokyoung Rhee, Jae-Min Shin, Kihang Choi, Sang-Kee Kang, Min-Kook Kim, Cheol-Heui Yun, Yun-Jaie Choi, Seung-Hoon Choi
• Keywords: BMCBI 2007, Intestinal, Intestinal Barrier-permeable Peptides, Intestinal Permeability