%A Vinod Kumar
%A Rajesh Kumar
%A Piyush Agrawal
%A Sumeet Patiyal
%A G.P.S. Raghava
%O Copyright of this article belongs to Frontiers Media SA.
%J Frontiers in pharmacology
%T A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure.
%X In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server "HemoPImod" to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).
%K  modified hemolytic peptides, machine learning, chemical descriptors, fingerprints, random forest, HemoPImod
%V 11
%D 2020
%I Frontiers Media SA
%L open2566