In Frontiers in pharmacology
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/).
Kumar Vinod, Kumar Rajesh, Agrawal Piyush, Patiyal Sumeet, Raghava Gajendra P S
HemoPImod, chemical descriptors, fingerprints, machine learning, modified hemolytic peptides, random forest