Citation: Qureshi A, Tandon H, Kumar M (2015)
AVP-IC50 Pred: Multiple machine learning techniques based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50) Biopolymers. 2015 Jul 25. doi: 10.1002/bip.22703. PMID:
26213387
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Antiviral peptides (AVPs) are a potential alternative strategy to explore new virus inhibiting candidates to control pathogenic viruses.
AVP-IC50Pred is a freely accessible regression based predictior of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)
Developed using peptides with quantitative and experimentally proven antiviral activity from AVPdb -Database of Antiviral Peptides.
and HIPdb -Database of HIV inhibiting peptides.
We have also utilized important features like amino acid composition, binary and physicochemical properties etc. for model development.
Multiple machine learning algorithms (SVM, Random Forest, IBk and K*) employed.
Highly user friendly web interface.
Additional analysis tools including Database scanning, BLAST, Map, Fragment, and Peptide properties calculator have also been included.
It would be helpful to researchers working on peptide based antiviral therapeutics development.
Related web servers:
AVPdb  -Database of antiviral peptides.
HIPdb -Database of HIV inhibiting peptides.
AVPpred -Prediction of antiviral peptides.
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