Publication: In silico approaches for the prediction and analysis of antiviral peptides: A review
Issued Date
2021-01-01
Resource Type
ISSN
18734286
13816128
13816128
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2-s2.0-85098553334
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Mahidol University
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SCOPUS
Bibliographic Citation
Current Pharmaceutical Design. Vol.27, No.18 (2021), 2180-2188
Suggested Citation
Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, Md Mehedi Hasan, Watshara Shoombuatong In silico approaches for the prediction and analysis of antiviral peptides: A review. Current Pharmaceutical Design. Vol.27, No.18 (2021), 2180-2188. doi:10.2174/1381612826666201102105827 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/78986
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Title
In silico approaches for the prediction and analysis of antiviral peptides: A review
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Abstract
In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic prop-erties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algo-rithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.