AI4AVP: An antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation

Tzu Tang Lin, Yih Yun Sun, Ching Tien Wang, Wen Chih Cheng, I. Hsuan Lu, Chung Yen Lin, Shu Hwa Chen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Motivation: Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results: We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community.

Original languageEnglish
Article numbervbac080
JournalBioinformatics Advances
Volume2
Issue number1
DOIs
Publication statusPublished - 2022

ASJC Scopus subject areas

  • Computer Science Applications
  • Genetics
  • Molecular Biology
  • Structural Biology

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