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Technical Report CMS-TR-20170331


Title Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors
Author/s Gunjan Mishra, Deepak Sehgal
Shiv Nadar University, India


VK Jayaraman
Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411 007, India
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 Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees 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); Descriptors; Extra Tree and Random Forests algorithm; Quantitative structure activity relationship (QSAR)
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Citing This Document Gunjan Mishra, Deepak Sehgal, and VK Jayaraman , Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors . Technical Report CMS-TR-20170331 of the Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411007, India (2017); available at http://cms.unipune.ac.in/reports/.
Notes, Published Reference, Etc. Published as Mishra et al., Bioinformation 13(3): 60-62 (2017).
Contact jayaraman AT cms.unipune.ac.in
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