Recent Advances in Machine Learning Models for Antiviral Peptide Prediction

This is the authors' abstract. We don't add key points for this paper.

Viral diseases are widespread and their impact is expressed in millions of cases of infection and mortality around the world, chronic viral diseases include COVID-19, HIV, and hepatitis. To prevent and treat these viral infections, novel agents, and antiviral peptides (AVPs) have been developed. Thus, identifying AVPs is crucial because these pieces of information are invaluable throughout the entire pharmaceutical industry and other sciences. This has been done experimentally and computationally but the need for better accurate and efficient predictors persists. This research also reviews current AVP predictors, including the datasets employed, feature representation methods, classification algorithms, and assessment criteria. In our paper, we discuss the weaknesses of the existing techniques, overview the most efficient strategies, and evaluate the benefits and drawbacks of the classifiers. Furthermore, some directions for future work are discussed in detail including enhanced feature representation techniques, selection of features, and classification approaches. The following advancements are targeted at improving the effectiveness of the algorithms used for the correct prediction of AVPs, which makes it possible to develop new antiviral drugs more successfully.