Title:In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review
Volume: 27
Issue: 18
Author(s): Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, Md. Mehedi Hasan and Watshara Shoombuatong*
Affiliation:
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700,Thailand
Keywords:
Therapeutic peptides, antiviral peptide, classification, machine learning, feature representation, feature selection.
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 properties.
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 algorithms,
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.
