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Title

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random
Forest and Extra Tree regressors

 

Authors

Gunjan Mishra1, Deepak Sehgal1 & Jayaraman K Valadi1, 2,*

 

Affiliation

1Shiv Nadar University, Gautam Budha Nagar, Uttar Pradesh 201314, India; 2Center for modelling and simulation, Savitri Bai Phule Pune university, Pune, Maharastra 411007, India;

 

Email

jayaraman.valadi@snu.edu.in

 

Article Type

Hypothesis

 

Date

Received March 6, 2017; Accepted March 16, 2017; Published March 31, 2017

 

Abstract

Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extra
Tree and Random Forest based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra Tree regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed
model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.

 

Keywords

Anti-Hepatitis peptide (AHP), Quantitative structure activity relationship (QSAR), Descriptors, Extra Tree and Random Forest algorithm

 

Citation

Mishra et al. Bioinformation 13(3): 60-62 (2017)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.