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

These days, machine-learning-based predictions of anti cancer drug show many attentions due to accuratelypredicting. Peptide-based machine learning techniques, which is a rapid and perfect outcome prediction, playan important role in developing peptide drugs. Anti cancer peptides that usually contain ۵ to ۳۰ amino acidresidues, can destroy cancer cells through apoptosis and necrosis that led to kill cells of cancer tumor. Itactions selectively without damaging other normal cells and show less systemic toxicity. They possess highhydrophobicity and a positive net charge. We study and use a systematic review on the application of machinelearning techniques and prediction of peptide drug activity. In this study computational tool constructionbased on machine learning algorithm were utilized to identify activity and interaction of peptide as an anticancer 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.