Developing an Antiviral Peptides Predictor with Generative Adversarial Network Data Augmentation

Abstract

MotivationNew antiviral drugs are urgently needed because of emerging viral pathogens increasing severity and drug resistance. Antiviral peptides (AVPs) have multiple antiviral properties and are appealing candidates for antiviral drug development. We developed a sequence-based binary classifier to identify whether an unknown short peptide has AVP activity. We collected AVP sequence data from six existing databases. We used a generative adversarial network to augment the number of AVPs in the positive training dataset and allow our deep convolutional neural network model to train on more data. ResultsOur classifier achieved outstanding performance on the testing dataset compared with other state-of-the-art classifiers. We deployed our trained classifier on a user-friendly web server. Availability and implementationAI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/ Contactcylin@iis.sinica.edu.tw Supplementary informationSupplementary data is also available.