RT Journal Article
SR Electronic
T1 Developing an Antiviral Peptides Predictor with Generative Adversarial Network Data Augmentation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.29.470292
DO 10.1101/2021.11.29.470292
A1 Tzu-Tang Lin
A1 Yi-Yun Sun
A1 Wei-Chih Cheng
A1 I-Hsuan Lu
A1 Shu-Hwa Chen
A1 Chung-Yen Lin
YR 2021
UL http://biorxiv.org/content/early/2021/11/30/2021.11.29.470292.abstract
AB Motivation New 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.Results Our 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 implementation AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/Contact cylin{at}iis.sinica.edu.twSupplementary information Supplementary data is also available.Competing Interest StatementThe authors have declared no competing interest.