1st International and 10th National Iranian Conference on Bioinformatics

Prediction peptide activity and interaction in drug discovery by utilize machine-learning technique

Paper ID : 1441-ICB10

Authors:

Mozhgan Shavandi *

Abstract:

These days, machine-learning-based predictions of anti cancer drug show many attentions due to accurately predicting. Peptide-based machine learning techniques, which is a rapid and perfect outcome prediction, play an important role in developing peptide drugs. Anti cancer peptides that usually contain 5 to 30 amino acid residues, can destroy cancer cells through apoptosis and necrosis that led to kill cells of cancer tumor. It actions selectively without damaging other normal cells and show less systemic toxicity. They possess high hydrophobicity and a positive net charge. We study and use a systematic review on the application of machine learning techniques and prediction of peptide drug activity. In this study computational tool construction based on machine learning algorithm were utilized to identify activity and interaction of peptide as an anti cancer drug. It has carried out by features calculated from the amino acid sequence and atomic composition. Using machine-learning approaches, we can develop prediction model for peptide drugs system.

Keywords:

Machine learning; Peptide drug; Anti cancer drug; drug discovery.

Status : Paper Accepted (Poster Presentation)