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PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network

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The continuous increase in pathogenic viruses and the intensive laboratory research emphasizes the need for cost- and time-efficient drug development. This accelerates research for alternate drug candidates like antiviral peptides (AVP) that have therapeutic and prophylactic potential and gaining attention in recent times. However, diversity in their sequences, limited and non-uniform characterization often limit their applications. Isolating newer peptide backbones with required characteristics is a cumbersome process with many design–test–build cycles. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful to expedite the initial stage of developing novel peptide drugs. In this study, we developed PandoraGAN that uses a manually curated training dataset of 130 highly active peptides that include peptides from known databases (such as AVPdb) and literature to generate novel antiviral peptides. The underlying architecture in PandoraGAN is able to learn a good representation of the implicit properties of antiviral peptides. The generated sequences from PandoraGAN are validated based on physico-chemical properties. They are also compared with the training dataset statistically using Pearson’s correlation and Mann–Whitney U-test. We, therefore, confirm that PandoraGAN is capable of generating a novel antiviral peptide backbone showing similar properties to that of the known highly active antiviral peptides. This approach exhibits a potential to discover novel patterns of AVP which may have not been seen earlier with traditional methods. To our knowledge, this is the first ever use of GAN models for antiviral peptides across the viral spectrum.

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Code and Data Availability

Antiviral sequences generated by PandoraGAN are available on the PandoraGAN server— The code and data is available at






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We would like to acknowledge Govt. of India for launching the Drug Discovery Hacakthon in July 2020 during the COVID-19 to develop computational methods for COVID-19 drug discovery and extrapolate these algorithms to other generic drug discovery challenges.


The authors declare no funds, grants, or other support was received.

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Authors and Affiliations



SS lead the development of the GAN architecture along with Divye S. PA conceptualized the problem and analyzed the data. DS provided domain guidance as well as manual dataset curation and JV provided overall guidance.

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Correspondence to Pooja Arora or Jayaraman Valadi.

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This study was conducted as part of the Drug discovery hackathon (DDH) 2020 organized by Govt. of India. The copyright and commercial aspects are to be followed as mentioned in DDH policies.

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Surana, S., Arora, P., Singh, D. et al. PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network. SN COMPUT. SCI. 4, 607 (2023).

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