AntiVPP 1.0: A portable tool for prediction of antiviral peptides

Computers in Biology and Medicine
Jorge Félix Beltrán LissabetJorge G Farias

Abstract

Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates. AntiVPP 1.0 is available at https://github.com/bio-coding/AntiVPP.

Citations

Nov 17, 2019·International Journal of Molecular Sciences·Nalini SchaduangratWatshara Shoombuatong
May 19, 2019·Cellular and Molecular Life Sciences : CMLS·Liana Costa Pereira Vilas BoasOctávio Luiz Franco
Jan 9, 2020·Interdisciplinary Sciences, Computational Life Sciences·Jorge Félix Beltrán LissabetJorge G Farias
Nov 8, 2020·Scientific Reports·Abu Sayed ChowdhuryBobbie-Jo M Webb-Robertson
May 8, 2021·Expert Opinion on Drug Discovery·Mateus Sá Magalhães SerafimKathia Maria Honorio

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Software Mentioned

AVPpred
HIPdb
Anaconda
joblib
WinPython
Python
sklearn
AVPs
RandomForestClassifier
pandas

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